Duckietown Challenges Home Challenges Submissions

Evaluator 5013

ID5013
evaluatorreg02
ownerI don't have one 😀
machinearchimede_c3f15323bf18
processreg02_c3f15323bf18
version6.2.5
first heard
last heard
statusinactive
# evaluating
# success38 62058
# timeout
# failed26 62065
# error1 62116
# aborted7 62258
# host-error12 62050
arm0
x86_641
Mac0
gpu available1
Number of processors40
Processor frequency (MHz)2.1 GHz
Free % of processors88%
RAM total (MB)376.6 GB
RAM free (MB)241.4 GB
Disk (MB)74209.1 GB
Disk available (MB)59013.2 GB
Docker Hub
P11
P2
Cloud simulations1
PI Camera0
# Duckiebots0
Map 3x3 avaiable
Number of duckies
gpu cores
AIDO 2 Map LF public
AIDO 2 Map LF private
AIDO 2 Map LFV public
AIDO 2 Map LFV private
AIDO 2 Map LFVI public
AIDO 2 Map LFVI private
AIDO 3 Map LF public
AIDO 3 Map LF private
AIDO 3 Map LFV public
AIDO 3 Map LFV private
AIDO 3 Map LFVI public
AIDO 3 Map LFVI private
AIDO 5 Map large loop
ETU track
for 2021, map is ETH_small_inter
IPFS mountpoint /ipfs available
IPNS mountpoint /ipns available

Evaluator jobs

Job IDsubmissionuseruser labelchallengestepstatusup to dateevaluatordate starteddate completeddurationmessage
6305514032YU CHENCBC V2, mar28 bc, mar31_apr6 anomaly aido-LFP-sim-validation350successyesreg020:11:53
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median6.874999999999984
in-drivable-lane_median2.6750000000000043
driven_lanedir_consec_median1.710897560905252
deviation-center-line_median0.35078201053652647


other stats
agent_compute-ego0_max0.15557641301836286
agent_compute-ego0_mean0.15156300381638765
agent_compute-ego0_median0.15064880332926986
agent_compute-ego0_min0.14937799558864803
complete-iteration_max0.6601814966575772
complete-iteration_mean0.5843315209678637
complete-iteration_median0.6069792886284526
complete-iteration_min0.46318600995697246
deviation-center-line_max0.529555831057489
deviation-center-line_mean0.3668637358682947
deviation-center-line_min0.23633509134263664
deviation-heading_max2.170328925725048
deviation-heading_mean1.6712708530125089
deviation-heading_median1.5859792119462628
deviation-heading_min1.3427960624324615
driven_any_max3.6355177367070537
driven_any_mean2.654083352976131
driven_any_median2.4444362108497444
driven_any_min2.0919432534979805
driven_lanedir_consec_max2.408865692840397
driven_lanedir_consec_mean1.7539915035954758
driven_lanedir_consec_min1.1853051997310022
driven_lanedir_max2.408865692840397
driven_lanedir_mean1.7539915035954758
driven_lanedir_median1.710897560905252
driven_lanedir_min1.1853051997310022
get_duckie_state_max0.09565005813326156
get_duckie_state_mean0.0710433619991823
get_duckie_state_median0.08649475202343981
get_duckie_state_min0.015533885816588018
get_robot_state_max0.014162217538187824
get_robot_state_mean0.013131937969682927
get_robot_state_median0.01299178854102346
get_robot_state_min0.012381957258496966
get_state_dump_max0.030659447397504536
get_state_dump_mean0.024876000060545148
get_state_dump_median0.025822007501495277
get_state_dump_min0.017200537841685497
get_ui_image_max0.0651553144641951
get_ui_image_mean0.057198112906418
get_ui_image_median0.0579834707944546
get_ui_image_min0.04767019557256768
in-drivable-lane_max3.449999999999989
in-drivable-lane_mean2.4249999999999985
in-drivable-lane_min0.8999999999999968
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.512247884368353, "get_ui_image": 0.05715076582772391, "step_physics": 0.204959545816694, "survival_time": 6.949999999999983, "driven_lanedir": 1.657139391319567, "get_state_dump": 0.030659447397504536, "get_robot_state": 0.012381957258496966, "sim_render-ego0": 0.01228536878313337, "get_duckie_state": 0.09565005813326156, "in-drivable-lane": 2.199999999999993, "deviation-heading": 1.4798720343357967, "agent_compute-ego0": 0.15557641301836286, "complete-iteration": 0.6026310886655535, "set_robot_commands": 0.00636211633682251, "deviation-center-line": 0.3646293804956887, "driven_lanedir_consec": 1.657139391319567, "sim_compute_sim_state": 0.020417390550885883, "sim_compute_performance-ego0": 0.006964669908796038}, "LFP-norm-zigzag-000-ego0": {"driven_any": 3.6355177367070537, "get_ui_image": 0.0651553144641951, "step_physics": 0.2578115323010613, "survival_time": 10.15000000000001, "driven_lanedir": 2.408865692840397, "get_state_dump": 0.0279464791802799, "get_robot_state": 0.013409753640492758, "sim_render-ego0": 0.011054298456977396, "get_duckie_state": 0.08802426562589757, "in-drivable-lane": 3.150000000000016, "deviation-heading": 2.170328925725048, "agent_compute-ego0": 0.15026911450367347, "complete-iteration": 0.6601814966575772, "set_robot_commands": 0.009878517365923116, "deviation-center-line": 0.529555831057489, "driven_lanedir_consec": 2.408865692840397, "sim_compute_sim_state": 0.02920422133277444, "sim_compute_performance-ego0": 0.007203561418196734}, "LFP-norm-techtrack-000-ego0": {"driven_any": 2.376624537331135, "get_ui_image": 0.058816175761185294, "step_physics": 0.22298614058907576, "survival_time": 6.299999999999986, "driven_lanedir": 1.7646557304909374, "get_state_dump": 0.023697535822710652, "get_robot_state": 0.014162217538187824, "sim_render-ego0": 0.012445840309924029, "get_duckie_state": 0.08496523842098207, "in-drivable-lane": 0.8999999999999968, "deviation-heading": 1.692086389556729, "agent_compute-ego0": 0.14937799558864803, "complete-iteration": 0.6113274885913519, "set_robot_commands": 0.008724163836381566, "deviation-center-line": 0.3369346405773643, "driven_lanedir_consec": 1.7646557304909374, "sim_compute_sim_state": 0.029151593606303056, "sim_compute_performance-ego0": 0.006776100068580447}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.0919432534979805, "get_ui_image": 0.04767019557256768, "step_physics": 0.16864428903064588, "survival_time": 6.799999999999984, "driven_lanedir": 1.1853051997310022, "get_state_dump": 0.017200537841685497, "get_robot_state": 0.01257382344155416, "sim_render-ego0": 0.012594318737948898, "get_duckie_state": 0.015533885816588018, "in-drivable-lane": 3.449999999999989, "deviation-heading": 1.3427960624324615, "agent_compute-ego0": 0.15102849215486625, "complete-iteration": 0.46318600995697246, "set_robot_commands": 0.01035955874589238, "deviation-center-line": 0.23633509134263664, "driven_lanedir_consec": 1.1853051997310022, "sim_compute_sim_state": 0.01872815354897158, "sim_compute_performance-ego0": 0.008599048113300853}}
set_robot_commands_max0.01035955874589238
set_robot_commands_mean0.008831089071254893
set_robot_commands_median0.00930134060115234
set_robot_commands_min0.00636211633682251
sim_compute_performance-ego0_max0.008599048113300853
sim_compute_performance-ego0_mean0.007385844877218518
sim_compute_performance-ego0_median0.0070841156634963865
sim_compute_performance-ego0_min0.006776100068580447
sim_compute_sim_state_max0.02920422133277444
sim_compute_sim_state_mean0.024375339759733744
sim_compute_sim_state_median0.02478449207859447
sim_compute_sim_state_min0.01872815354897158
sim_render-ego0_max0.012594318737948898
sim_render-ego0_mean0.012094956571995923
sim_render-ego0_median0.0123656045465287
sim_render-ego0_min0.011054298456977396
simulation-passed1
step_physics_max0.2578115323010613
step_physics_mean0.21360037693436923
step_physics_median0.21397284320288487
step_physics_min0.16864428903064588
survival_time_max10.15000000000001
survival_time_mean7.54999999999999
survival_time_min6.299999999999986
No reset possible
6285913925Patrick Genevatemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:41
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A929b5188a9b5978ea78814123e2e9dc311d26d3ce658c7d75a975d0e04f6503d&fromImage=docker.io%2Fgoldbattle%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for goldbattle/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/goldbattle/aido-submissions@sha256:929b5188a9b5978ea78814123e2e9dc311d26d3ce658c7d75a975d0e04f6503d  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/goldbattle/aido-submissions@sha256:929b5188a9b5978ea78814123e2e9dc311d26d3ce658c7d75a975d0e04f6503d
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6271113870Peder Seflandtemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6271013870Peder Seflandtemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:37
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6268413866Peder Seflandtemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6268313866Peder Seflandtemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6268213866Peder Seflandtemplate-randomaido-hello-sim-validation370host-erroryesreg020:00:35
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6266013866Peder Seflandtemplate-randomaido-hello-sim-validation370abortedyesreg020:00:35
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6265913866Peder Seflandtemplate-randomaido-hello-sim-validation370abortedyesreg020:00:35
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3A2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809&fromImage=docker.io%2Fh0mlab%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for h0mlab/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/h0mlab/aido-submissions@sha256:2a8b625a92c565f6505a4c3cc11e9240bd29d6c68007963ac067d49418699809
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6259613842Gopi Palaniappantemplate-randomaido-hello-sim-validation370successyesreg020:01:44
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median2.1500000000000004
in-drivable-lane_median1.15
driven_lanedir_consec_median0.2357331065993169
deviation-center-line_median0.05863921966719952


other stats
agent_compute-ego0_max0.032821042971177536
agent_compute-ego0_mean0.032821042971177536
agent_compute-ego0_median0.032821042971177536
agent_compute-ego0_min0.032821042971177536
complete-iteration_max0.3010998043147
complete-iteration_mean0.3010998043147
complete-iteration_median0.3010998043147
complete-iteration_min0.3010998043147
deviation-center-line_max0.05863921966719952
deviation-center-line_mean0.05863921966719952
deviation-center-line_min0.05863921966719952
deviation-heading_max0.46108604520141144
deviation-heading_mean0.46108604520141144
deviation-heading_median0.46108604520141144
deviation-heading_min0.46108604520141144
driven_any_max0.4603380961060899
driven_any_mean0.4603380961060899
driven_any_median0.4603380961060899
driven_any_min0.4603380961060899
driven_lanedir_consec_max0.2357331065993169
driven_lanedir_consec_mean0.2357331065993169
driven_lanedir_consec_min0.2357331065993169
driven_lanedir_max0.2357331065993169
driven_lanedir_mean0.2357331065993169
driven_lanedir_median0.2357331065993169
driven_lanedir_min0.2357331065993169
get_duckie_state_max0.0204493295062672
get_duckie_state_mean0.0204493295062672
get_duckie_state_median0.0204493295062672
get_duckie_state_min0.0204493295062672
get_robot_state_max0.012748756191947245
get_robot_state_mean0.012748756191947245
get_robot_state_median0.012748756191947245
get_robot_state_min0.012748756191947245
get_state_dump_max0.017933357845653187
get_state_dump_mean0.017933357845653187
get_state_dump_median0.017933357845653187
get_state_dump_min0.017933357845653187
get_ui_image_max0.04723713072863492
get_ui_image_mean0.04723713072863492
get_ui_image_median0.04723713072863492
get_ui_image_min0.04723713072863492
in-drivable-lane_max1.15
in-drivable-lane_mean1.15
in-drivable-lane_min1.15
per-episodes
details{"hello-norm-small_loop-000-ego0": {"driven_any": 0.4603380961060899, "get_ui_image": 0.04723713072863492, "step_physics": 0.11703097278421576, "survival_time": 2.1500000000000004, "driven_lanedir": 0.2357331065993169, "get_state_dump": 0.017933357845653187, "get_robot_state": 0.012748756191947245, "sim_render-ego0": 0.011904770677739923, "get_duckie_state": 0.0204493295062672, "in-drivable-lane": 1.15, "deviation-heading": 0.46108604520141144, "agent_compute-ego0": 0.032821042971177536, "complete-iteration": 0.3010998043147, "set_robot_commands": 0.013516225598075172, "deviation-center-line": 0.05863921966719952, "driven_lanedir_consec": 0.2357331065993169, "sim_compute_sim_state": 0.01757814125581221, "sim_compute_performance-ego0": 0.009653882546858356}}
set_robot_commands_max0.013516225598075172
set_robot_commands_mean0.013516225598075172
set_robot_commands_median0.013516225598075172
set_robot_commands_min0.013516225598075172
sim_compute_performance-ego0_max0.009653882546858356
sim_compute_performance-ego0_mean0.009653882546858356
sim_compute_performance-ego0_median0.009653882546858356
sim_compute_performance-ego0_min0.009653882546858356
sim_compute_sim_state_max0.01757814125581221
sim_compute_sim_state_mean0.01757814125581221
sim_compute_sim_state_median0.01757814125581221
sim_compute_sim_state_min0.01757814125581221
sim_render-ego0_max0.011904770677739923
sim_render-ego0_mean0.011904770677739923
sim_render-ego0_median0.011904770677739923
sim_render-ego0_min0.011904770677739923
simulation-passed1
step_physics_max0.11703097278421576
step_physics_mean0.11703097278421576
step_physics_median0.11703097278421576
step_physics_min0.11703097278421576
survival_time_max2.1500000000000004
survival_time_mean2.1500000000000004
survival_time_min2.1500000000000004
No reset possible
6257513835Christian Becktemplate-randomaido-hello-sim-validation370successyesreg020:01:44
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median2.1500000000000004
in-drivable-lane_median1.15
driven_lanedir_consec_median0.2357331065993169
deviation-center-line_median0.05863921966719952


other stats
agent_compute-ego0_max0.03965851935473355
agent_compute-ego0_mean0.03965851935473355
agent_compute-ego0_median0.03965851935473355
agent_compute-ego0_min0.03965851935473355
complete-iteration_max0.29540821638974274
complete-iteration_mean0.29540821638974274
complete-iteration_median0.29540821638974274
complete-iteration_min0.29540821638974274
deviation-center-line_max0.05863921966719952
deviation-center-line_mean0.05863921966719952
deviation-center-line_min0.05863921966719952
deviation-heading_max0.46108604520141144
deviation-heading_mean0.46108604520141144
deviation-heading_median0.46108604520141144
deviation-heading_min0.46108604520141144
driven_any_max0.4603380961060899
driven_any_mean0.4603380961060899
driven_any_median0.4603380961060899
driven_any_min0.4603380961060899
driven_lanedir_consec_max0.2357331065993169
driven_lanedir_consec_mean0.2357331065993169
driven_lanedir_consec_min0.2357331065993169
driven_lanedir_max0.2357331065993169
driven_lanedir_mean0.2357331065993169
driven_lanedir_median0.2357331065993169
driven_lanedir_min0.2357331065993169
get_duckie_state_max0.017410310831936924
get_duckie_state_mean0.017410310831936924
get_duckie_state_median0.017410310831936924
get_duckie_state_min0.017410310831936924
get_robot_state_max0.01641800728711215
get_robot_state_mean0.01641800728711215
get_robot_state_median0.01641800728711215
get_robot_state_min0.01641800728711215
get_state_dump_max0.019070121374997227
get_state_dump_mean0.019070121374997227
get_state_dump_median0.019070121374997227
get_state_dump_min0.019070121374997227
get_ui_image_max0.0460990233854814
get_ui_image_mean0.0460990233854814
get_ui_image_median0.0460990233854814
get_ui_image_min0.0460990233854814
in-drivable-lane_max1.15
in-drivable-lane_mean1.15
in-drivable-lane_min1.15
per-episodes
details{"hello-norm-small_loop-000-ego0": {"driven_any": 0.4603380961060899, "get_ui_image": 0.0460990233854814, "step_physics": 0.10638183897191827, "survival_time": 2.1500000000000004, "driven_lanedir": 0.2357331065993169, "get_state_dump": 0.019070121374997227, "get_robot_state": 0.01641800728711215, "sim_render-ego0": 0.014617643573067406, "get_duckie_state": 0.017410310831936924, "in-drivable-lane": 1.15, "deviation-heading": 0.46108604520141144, "agent_compute-ego0": 0.03965851935473355, "complete-iteration": 0.29540821638974274, "set_robot_commands": 0.007910441268574108, "deviation-center-line": 0.05863921966719952, "driven_lanedir_consec": 0.2357331065993169, "sim_compute_sim_state": 0.019698289307680996, "sim_compute_performance-ego0": 0.007922134616158226}}
set_robot_commands_max0.007910441268574108
set_robot_commands_mean0.007910441268574108
set_robot_commands_median0.007910441268574108
set_robot_commands_min0.007910441268574108
sim_compute_performance-ego0_max0.007922134616158226
sim_compute_performance-ego0_mean0.007922134616158226
sim_compute_performance-ego0_median0.007922134616158226
sim_compute_performance-ego0_min0.007922134616158226
sim_compute_sim_state_max0.019698289307680996
sim_compute_sim_state_mean0.019698289307680996
sim_compute_sim_state_median0.019698289307680996
sim_compute_sim_state_min0.019698289307680996
sim_render-ego0_max0.014617643573067406
sim_render-ego0_mean0.014617643573067406
sim_render-ego0_median0.014617643573067406
sim_render-ego0_min0.014617643573067406
simulation-passed1
step_physics_max0.10638183897191827
step_physics_mean0.10638183897191827
step_physics_median0.10638183897191827
step_physics_min0.10638183897191827
survival_time_max2.1500000000000004
survival_time_mean2.1500000000000004
survival_time_min2.1500000000000004
No reset possible
6248913800Raymond Pfafftemplate-randomaido-hello-sim-validation370successyesreg020:01:48
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median2.1500000000000004
in-drivable-lane_median1.15
driven_lanedir_consec_median0.2357331065993169
deviation-center-line_median0.05863921966719952


other stats
agent_compute-ego0_max0.032694626938213005
agent_compute-ego0_mean0.032694626938213005
agent_compute-ego0_median0.032694626938213005
agent_compute-ego0_min0.032694626938213005
complete-iteration_max0.2660932649265636
complete-iteration_mean0.2660932649265636
complete-iteration_median0.2660932649265636
complete-iteration_min0.2660932649265636
deviation-center-line_max0.05863921966719952
deviation-center-line_mean0.05863921966719952
deviation-center-line_min0.05863921966719952
deviation-heading_max0.46108604520141144
deviation-heading_mean0.46108604520141144
deviation-heading_median0.46108604520141144
deviation-heading_min0.46108604520141144
driven_any_max0.4603380961060899
driven_any_mean0.4603380961060899
driven_any_median0.4603380961060899
driven_any_min0.4603380961060899
driven_lanedir_consec_max0.2357331065993169
driven_lanedir_consec_mean0.2357331065993169
driven_lanedir_consec_min0.2357331065993169
driven_lanedir_max0.2357331065993169
driven_lanedir_mean0.2357331065993169
driven_lanedir_median0.2357331065993169
driven_lanedir_min0.2357331065993169
get_duckie_state_max0.01187355409968983
get_duckie_state_mean0.01187355409968983
get_duckie_state_median0.01187355409968983
get_duckie_state_min0.01187355409968983
get_robot_state_max0.010834043676202948
get_robot_state_mean0.010834043676202948
get_robot_state_median0.010834043676202948
get_robot_state_min0.010834043676202948
get_state_dump_max0.01917796243320812
get_state_dump_mean0.01917796243320812
get_state_dump_median0.01917796243320812
get_state_dump_min0.01917796243320812
get_ui_image_max0.045136787674643776
get_ui_image_mean0.045136787674643776
get_ui_image_median0.045136787674643776
get_ui_image_min0.045136787674643776
in-drivable-lane_max1.15
in-drivable-lane_mean1.15
in-drivable-lane_min1.15
per-episodes
details{"hello-norm-small_loop-000-ego0": {"driven_any": 0.4603380961060899, "get_ui_image": 0.045136787674643776, "step_physics": 0.10873993960293855, "survival_time": 2.1500000000000004, "driven_lanedir": 0.2357331065993169, "get_state_dump": 0.01917796243320812, "get_robot_state": 0.010834043676202948, "sim_render-ego0": 0.011182980103926226, "get_duckie_state": 0.01187355409968983, "in-drivable-lane": 1.15, "deviation-heading": 0.46108604520141144, "agent_compute-ego0": 0.032694626938213005, "complete-iteration": 0.2660932649265636, "set_robot_commands": 0.007163020697506991, "deviation-center-line": 0.05863921966719952, "driven_lanedir_consec": 0.2357331065993169, "sim_compute_sim_state": 0.012938737869262695, "sim_compute_performance-ego0": 0.006081927906383167}}
set_robot_commands_max0.007163020697506991
set_robot_commands_mean0.007163020697506991
set_robot_commands_median0.007163020697506991
set_robot_commands_min0.007163020697506991
sim_compute_performance-ego0_max0.006081927906383167
sim_compute_performance-ego0_mean0.006081927906383167
sim_compute_performance-ego0_median0.006081927906383167
sim_compute_performance-ego0_min0.006081927906383167
sim_compute_sim_state_max0.012938737869262695
sim_compute_sim_state_mean0.012938737869262695
sim_compute_sim_state_median0.012938737869262695
sim_compute_sim_state_min0.012938737869262695
sim_render-ego0_max0.011182980103926226
sim_render-ego0_mean0.011182980103926226
sim_render-ego0_median0.011182980103926226
sim_render-ego0_min0.011182980103926226
simulation-passed1
step_physics_max0.10873993960293855
step_physics_mean0.10873993960293855
step_physics_median0.10873993960293855
step_physics_min0.10873993960293855
survival_time_max2.1500000000000004
survival_time_mean2.1500000000000004
survival_time_min2.1500000000000004
No reset possible
6247313798Nicholas Kostelniktemplate-randomaido-hello-sim-validation370abortedyesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3Ab13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691&fromImage=docker.io%2Fnitaigao%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for nitaigao/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6247213798Nicholas Kostelniktemplate-randomaido-hello-sim-validation370abortedyesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3Ab13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691&fromImage=docker.io%2Fnitaigao%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for nitaigao/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6247113798Nicholas Kostelniktemplate-randomaido-hello-sim-validation370abortedyesreg020:00:36
Uncaught exception: [...]
Uncaught exception:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 261, in _raise_for_status
    response.raise_for_status()
  File "/usr/local/lib/python3.8/dist-packages/requests/models.py", line 941, in raise_for_status
    raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 404 Client Error: Not Found for url: http+docker://localhost/v1.35/images/create?tag=sha256%3Ab13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691&fromImage=docker.io%2Fnitaigao%2Faido-submissions

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 65, in docker_pull
    pulling = client.api.pull(repository=repository, tag=br.tag, stream=True, decode=True)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/image.py", line 415, in pull
    self._raise_for_status(response)
  File "/usr/local/lib/python3.8/dist-packages/docker/api/client.py", line 263, in _raise_for_status
    raise create_api_error_from_http_exception(e)
  File "/usr/local/lib/python3.8/dist-packages/docker/errors.py", line 31, in create_api_error_from_http_exception
    raise cls(e, response=response, explanation=explanation)
docker.errors.ImageNotFound: 404 Client Error: Not Found ("pull access denied for nitaigao/aido-submissions, repository does not exist or may require 'docker login': denied: requested access to the resource is denied")

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 38, in docker_pull_retry
    return docker_pull(client, image_name, quiet=quiet)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 84, in docker_pull
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: Cannot pull repo  docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691  tag  None

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 777, in get_cr
    cr = run_single(
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 976, in run_single
    docker_pull_retry(client, image, ntimes=4, wait=5)
  File "/usr/local/lib/python3.8/dist-packages/duckietown_build_utils/docker_pulling.py", line 42, in docker_pull_retry
    raise PullError(msg) from e
duckietown_build_utils.docker_pulling.PullError: After trying 4 I still could not pull docker.io/nitaigao/aido-submissions@sha256:b13078d04947eb3a802ebc4e9db985f6a60c2a3cae145d65fbd44ef0177e1691
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6244013788Tass Otertemplate-randomaido-hello-sim-validation370successyesreg020:01:47
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median2.1500000000000004
in-drivable-lane_median1.15
driven_lanedir_consec_median0.2357331065993169
deviation-center-line_median0.05863921966719952


other stats
agent_compute-ego0_max0.040158244696530426
agent_compute-ego0_mean0.040158244696530426
agent_compute-ego0_median0.040158244696530426
agent_compute-ego0_min0.040158244696530426
complete-iteration_max0.2877045382152904
complete-iteration_mean0.2877045382152904
complete-iteration_median0.2877045382152904
complete-iteration_min0.2877045382152904
deviation-center-line_max0.05863921966719952
deviation-center-line_mean0.05863921966719952
deviation-center-line_min0.05863921966719952
deviation-heading_max0.46108604520141144
deviation-heading_mean0.46108604520141144
deviation-heading_median0.46108604520141144
deviation-heading_min0.46108604520141144
driven_any_max0.4603380961060899
driven_any_mean0.4603380961060899
driven_any_median0.4603380961060899
driven_any_min0.4603380961060899
driven_lanedir_consec_max0.2357331065993169
driven_lanedir_consec_mean0.2357331065993169
driven_lanedir_consec_min0.2357331065993169
driven_lanedir_max0.2357331065993169
driven_lanedir_mean0.2357331065993169
driven_lanedir_median0.2357331065993169
driven_lanedir_min0.2357331065993169
get_duckie_state_max0.013264401392503218
get_duckie_state_mean0.013264401392503218
get_duckie_state_median0.013264401392503218
get_duckie_state_min0.013264401392503218
get_robot_state_max0.013390887867320667
get_robot_state_mean0.013390887867320667
get_robot_state_median0.013390887867320667
get_robot_state_min0.013390887867320667
get_state_dump_max0.01958321983164007
get_state_dump_mean0.01958321983164007
get_state_dump_median0.01958321983164007
get_state_dump_min0.01958321983164007
get_ui_image_max0.04899234663356434
get_ui_image_mean0.04899234663356434
get_ui_image_median0.04899234663356434
get_ui_image_min0.04899234663356434
in-drivable-lane_max1.15
in-drivable-lane_mean1.15
in-drivable-lane_min1.15
per-episodes
details{"hello-norm-small_loop-000-ego0": {"driven_any": 0.4603380961060899, "get_ui_image": 0.04899234663356434, "step_physics": 0.10534048080444336, "survival_time": 2.1500000000000004, "driven_lanedir": 0.2357331065993169, "get_state_dump": 0.01958321983164007, "get_robot_state": 0.013390887867320667, "sim_render-ego0": 0.011238043958490544, "get_duckie_state": 0.013264401392503218, "in-drivable-lane": 1.15, "deviation-heading": 0.46108604520141144, "agent_compute-ego0": 0.040158244696530426, "complete-iteration": 0.2877045382152904, "set_robot_commands": 0.009259717030958696, "deviation-center-line": 0.05863921966719952, "driven_lanedir_consec": 0.2357331065993169, "sim_compute_sim_state": 0.01827640966935591, "sim_compute_performance-ego0": 0.007958059961145575}}
set_robot_commands_max0.009259717030958696
set_robot_commands_mean0.009259717030958696
set_robot_commands_median0.009259717030958696
set_robot_commands_min0.009259717030958696
sim_compute_performance-ego0_max0.007958059961145575
sim_compute_performance-ego0_mean0.007958059961145575
sim_compute_performance-ego0_median0.007958059961145575
sim_compute_performance-ego0_min0.007958059961145575
sim_compute_sim_state_max0.01827640966935591
sim_compute_sim_state_mean0.01827640966935591
sim_compute_sim_state_median0.01827640966935591
sim_compute_sim_state_min0.01827640966935591
sim_render-ego0_max0.011238043958490544
sim_render-ego0_mean0.011238043958490544
sim_render-ego0_median0.011238043958490544
sim_render-ego0_min0.011238043958490544
simulation-passed1
step_physics_max0.10534048080444336
step_physics_mean0.10534048080444336
step_physics_median0.10534048080444336
step_physics_min0.10534048080444336
survival_time_max2.1500000000000004
survival_time_mean2.1500000000000004
survival_time_min2.1500000000000004
No reset possible
6238313763Andrea Censi 🇨🇭straightaido-hello-sim-validation370successnoreg020:02:21
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median4.199999999999993
in-drivable-lane_median0.0
driven_lanedir_consec_median0.5156583766631895
deviation-center-line_median0.11159597685449854


other stats
agent_compute-ego0_max0.032339985230389764
agent_compute-ego0_mean0.032339985230389764
agent_compute-ego0_median0.032339985230389764
agent_compute-ego0_min0.032339985230389764
complete-iteration_max0.32305496159721825
complete-iteration_mean0.32305496159721825
complete-iteration_median0.32305496159721825
complete-iteration_min0.32305496159721825
deviation-center-line_max0.11159597685449854
deviation-center-line_mean0.11159597685449854
deviation-center-line_min0.11159597685449854
deviation-heading_max0.5550782670524196
deviation-heading_mean0.5550782670524196
deviation-heading_median0.5550782670524196
deviation-heading_min0.5550782670524196
driven_any_max0.518718406586057
driven_any_mean0.518718406586057
driven_any_median0.518718406586057
driven_any_min0.518718406586057
driven_lanedir_consec_max0.5156583766631895
driven_lanedir_consec_mean0.5156583766631895
driven_lanedir_consec_min0.5156583766631895
driven_lanedir_max0.5156583766631895
driven_lanedir_mean0.5156583766631895
driven_lanedir_median0.5156583766631895
driven_lanedir_min0.5156583766631895
get_duckie_state_max0.053798891516292795
get_duckie_state_mean0.053798891516292795
get_duckie_state_median0.053798891516292795
get_duckie_state_min0.053798891516292795
get_robot_state_max0.01636351417092716
get_robot_state_mean0.01636351417092716
get_robot_state_median0.01636351417092716
get_robot_state_min0.01636351417092716
get_state_dump_max0.022936147802016315
get_state_dump_mean0.022936147802016315
get_state_dump_median0.022936147802016315
get_state_dump_min0.022936147802016315
get_ui_image_max0.0503745247335995
get_ui_image_mean0.0503745247335995
get_ui_image_median0.0503745247335995
get_ui_image_min0.0503745247335995
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"hello-norm-small_loop-000-ego0": {"driven_any": 0.518718406586057, "get_ui_image": 0.0503745247335995, "step_physics": 0.09993023872375488, "survival_time": 4.199999999999993, "driven_lanedir": 0.5156583766631895, "get_state_dump": 0.022936147802016315, "get_robot_state": 0.01636351417092716, "sim_render-ego0": 0.01349167543299058, "get_duckie_state": 0.053798891516292795, "in-drivable-lane": 0.0, "deviation-heading": 0.5550782670524196, "agent_compute-ego0": 0.032339985230389764, "complete-iteration": 0.32305496159721825, "set_robot_commands": 0.009257114634794348, "deviation-center-line": 0.11159597685449854, "driven_lanedir_consec": 0.5156583766631895, "sim_compute_sim_state": 0.016949993021347944, "sim_compute_performance-ego0": 0.007351502250222599}}
set_robot_commands_max0.009257114634794348
set_robot_commands_mean0.009257114634794348
set_robot_commands_median0.009257114634794348
set_robot_commands_min0.009257114634794348
sim_compute_performance-ego0_max0.007351502250222599
sim_compute_performance-ego0_mean0.007351502250222599
sim_compute_performance-ego0_median0.007351502250222599
sim_compute_performance-ego0_min0.007351502250222599
sim_compute_sim_state_max0.016949993021347944
sim_compute_sim_state_mean0.016949993021347944
sim_compute_sim_state_median0.016949993021347944
sim_compute_sim_state_min0.016949993021347944
sim_render-ego0_max0.01349167543299058
sim_render-ego0_mean0.01349167543299058
sim_render-ego0_median0.01349167543299058
sim_render-ego0_min0.01349167543299058
simulation-passed1
step_physics_max0.09993023872375488
step_physics_mean0.09993023872375488
step_physics_median0.09993023872375488
step_physics_min0.09993023872375488
survival_time_max4.199999999999993
survival_time_mean4.199999999999993
survival_time_min4.199999999999993
No reset possible
6237813759Frank (Chude) Qian 🇨🇦CBC Net v1 - Best Lossaido-LFP-sim-validation350abortedyesreg020:07:43
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median4.92499999999999
in-drivable-lane_median0.574999999999998
driven_lanedir_consec_median1.078366209811518
deviation-center-line_median0.2301051484803983


other stats
agent_compute-ego0_max0.10981068866593498
agent_compute-ego0_mean0.1015037928462827
agent_compute-ego0_median0.100795637504041
agent_compute-ego0_min0.09461320771111384
complete-iteration_max0.6045013538428715
complete-iteration_mean0.5035355524801147
complete-iteration_median0.5216493968286362
complete-iteration_min0.3663420624203152
deviation-center-line_max0.3497599943191688
deviation-center-line_mean0.22948410944673656
deviation-center-line_min0.10796614650698064
deviation-heading_max1.449932893760358
deviation-heading_mean1.0689835811682782
deviation-heading_median1.1432419019775235
deviation-heading_min0.5395176269577073
driven_any_max2.127949823075878
driven_any_mean1.4392358704954915
driven_any_median1.547766445569265
driven_any_min0.5334607677675589
driven_lanedir_consec_max1.7921273131555495
driven_lanedir_consec_mean1.112162344806759
driven_lanedir_consec_min0.4997896464484508
driven_lanedir_max1.7921273131555495
driven_lanedir_mean1.112162344806759
driven_lanedir_median1.078366209811518
driven_lanedir_min0.4997896464484508
get_duckie_state_max0.09713983973231884
get_duckie_state_mean0.06685883261866983
get_duckie_state_median0.07745392616621401
get_duckie_state_min0.015387638409932456
get_robot_state_max0.01340262166091374
get_robot_state_mean0.012131456901840976
get_robot_state_median0.012047513706765937
get_robot_state_min0.011028178532918294
get_state_dump_max0.03682596981525421
get_state_dump_mean0.028661659133857584
get_state_dump_median0.02887052566957361
get_state_dump_min0.02007961538102892
get_ui_image_max0.06596504577568599
get_ui_image_mean0.0568600688448106
get_ui_image_median0.05780036466444912
get_ui_image_min0.04587450027465821
in-drivable-lane_max2.6999999999999913
in-drivable-lane_mean0.9624999999999968
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.127949823075878, "get_ui_image": 0.05432129999913207, "step_physics": 0.17339632926730936, "survival_time": 5.399999999999989, "driven_lanedir": 1.7921273131555495, "get_state_dump": 0.032656087787873156, "get_robot_state": 0.012522948991268053, "sim_render-ego0": 0.010143973411770042, "get_duckie_state": 0.09713983973231884, "in-drivable-lane": 0.4999999999999982, "deviation-heading": 1.185064742160636, "agent_compute-ego0": 0.10694846100763444, "complete-iteration": 0.5226839135546203, "set_robot_commands": 0.007553748034555978, "deviation-center-line": 0.3497599943191688, "driven_lanedir_consec": 1.7921273131555495, "sim_compute_sim_state": 0.021234164544201777, "sim_compute_performance-ego0": 0.006513026876187105}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5334607677675589, "get_ui_image": 0.06127942932976617, "step_physics": 0.1963036413545962, "survival_time": 2.6499999999999986, "driven_lanedir": 0.4997896464484508, "get_state_dump": 0.025084963551274053, "get_robot_state": 0.011572078422263815, "sim_render-ego0": 0.014231085777282717, "get_duckie_state": 0.07653826254385489, "in-drivable-lane": 0.0, "deviation-heading": 1.1014190617944113, "agent_compute-ego0": 0.09461320771111384, "complete-iteration": 0.5206148801026521, "set_robot_commands": 0.00924274656507704, "deviation-center-line": 0.2037958598628972, "driven_lanedir_consec": 0.4997896464484508, "sim_compute_sim_state": 0.022909786966111925, "sim_compute_performance-ego0": 0.00858938252484357}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.811286963800419, "get_ui_image": 0.06596504577568599, "step_physics": 0.23026799942765916, "survival_time": 5.549999999999988, "driven_lanedir": 1.502065148021555, "get_state_dump": 0.03682596981525421, "get_robot_state": 0.01340262166091374, "sim_render-ego0": 0.013046607375144958, "get_duckie_state": 0.07836958978857313, "in-drivable-lane": 0.6499999999999977, "deviation-heading": 1.449932893760358, "agent_compute-ego0": 0.10981068866593498, "complete-iteration": 0.6045013538428715, "set_robot_commands": 0.010606359158243452, "deviation-center-line": 0.2564144370978994, "driven_lanedir_consec": 1.502065148021555, "sim_compute_sim_state": 0.03499307802745274, "sim_compute_performance-ego0": 0.01096510248524802}, "LFP-norm-small_loop-000-ego0": {"driven_any": 1.284245927338111, "get_ui_image": 0.04587450027465821, "step_physics": 0.1352629237704807, "survival_time": 4.449999999999992, "driven_lanedir": 0.6546672716014805, "get_state_dump": 0.02007961538102892, "get_robot_state": 0.011028178532918294, "sim_render-ego0": 0.00998327996995714, "get_duckie_state": 0.015387638409932456, "in-drivable-lane": 2.6999999999999913, "deviation-heading": 0.5395176269577073, "agent_compute-ego0": 0.0946428140004476, "complete-iteration": 0.3663420624203152, "set_robot_commands": 0.007193371984693739, "deviation-center-line": 0.10796614650698064, "driven_lanedir_consec": 0.6546672716014805, "sim_compute_sim_state": 0.01836709976196289, "sim_compute_performance-ego0": 0.008288786146375867}}
set_robot_commands_max0.010606359158243452
set_robot_commands_mean0.008649056435642552
set_robot_commands_median0.00839824729981651
set_robot_commands_min0.007193371984693739
sim_compute_performance-ego0_max0.01096510248524802
sim_compute_performance-ego0_mean0.008589074508163642
sim_compute_performance-ego0_median0.008439084335609718
sim_compute_performance-ego0_min0.006513026876187105
sim_compute_sim_state_max0.03499307802745274
sim_compute_sim_state_mean0.024376032324932333
sim_compute_sim_state_median0.022071975755156847
sim_compute_sim_state_min0.01836709976196289
sim_render-ego0_max0.014231085777282717
sim_render-ego0_mean0.011851236633538714
sim_render-ego0_median0.0115952903934575
sim_render-ego0_min0.00998327996995714
simulation-passed1
step_physics_max0.23026799942765916
step_physics_mean0.18380772345501137
step_physics_median0.18484998531095276
step_physics_min0.1352629237704807
survival_time_max5.549999999999988
survival_time_mean4.512499999999992
survival_time_min2.6499999999999986
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6236713754Frank (Chude) Qian 🇨🇦CBC Net v1aido-LF-sim-validation347successyesreg020:40:46
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driven_lanedir_consec_median13.087925508056257
survival_time_median44.89999999999951
deviation-center-line_median2.8049197211703527
in-drivable-lane_median5.825000000000035


other stats
agent_compute-ego0_max0.09524312927609398
agent_compute-ego0_mean0.09345337015344692
agent_compute-ego0_median0.09423379824620104
agent_compute-ego0_min0.09010275484529172
complete-iteration_max0.44320640452143734
complete-iteration_mean0.42761473049400034
complete-iteration_median0.429346375024686
complete-iteration_min0.40855976740519206
deviation-center-line_max4.591843933816273
deviation-center-line_mean2.5788278750385656
deviation-center-line_min0.11362812399728472
deviation-heading_max21.363012825327445
deviation-heading_mean10.302930625784011
deviation-heading_median9.562096389437963
deviation-heading_min0.7245168989326686
driven_any_max27.205850997519203
driven_any_mean16.24502348158073
driven_any_median18.02672308708908
driven_any_min1.72079675462557
driven_lanedir_consec_max24.83495469004709
driven_lanedir_consec_mean12.907479038817163
driven_lanedir_consec_min0.6191104491090598
driven_lanedir_max24.83495469004709
driven_lanedir_mean12.907479038817163
driven_lanedir_median13.087925508056257
driven_lanedir_min0.6191104491090598
get_duckie_state_max4.00785403287381e-06
get_duckie_state_mean3.896828931136047e-06
get_duckie_state_median3.932302351025528e-06
get_duckie_state_min3.71485698961932e-06
get_robot_state_max0.013819126761227624
get_robot_state_mean0.012829100865197176
get_robot_state_median0.013081694299670574
get_robot_state_min0.011333888100219932
get_state_dump_max0.020228182247139632
get_state_dump_mean0.01736393512562341
get_state_dump_median0.017292487694569494
get_state_dump_min0.014642582866215026
get_ui_image_max0.0642131351289295
get_ui_image_mean0.05387491990348181
get_ui_image_median0.05217325490620853
get_ui_image_min0.04694003467258069
in-drivable-lane_max9.29999999999977
in-drivable-lane_mean6.074999999999957
in-drivable-lane_min3.3499999999999885
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 27.205850997519203, "get_ui_image": 0.05010327987130933, "step_physics": 0.20406513329251025, "survival_time": 59.99999999999873, "driven_lanedir": 24.83495469004709, "get_state_dump": 0.020228182247139632, "get_robot_state": 0.013536969986088963, "sim_render-ego0": 0.010859133500441424, "get_duckie_state": 4.00785403287381e-06, "in-drivable-lane": 3.999999999999961, "deviation-heading": 11.384700885403191, "agent_compute-ego0": 0.09357896772252829, "complete-iteration": 0.4333869243640884, "set_robot_commands": 0.009265205445237998, "deviation-center-line": 4.591843933816273, "driven_lanedir_consec": 24.83495469004709, "sim_compute_sim_state": 0.024420758667436863, "sim_compute_performance-ego0": 0.007131007589170279}, "LF-norm-zigzag-000-ego0": {"driven_any": 1.72079675462557, "get_ui_image": 0.0642131351289295, "step_physics": 0.17424098423549106, "survival_time": 5.1999999999999895, "driven_lanedir": 0.6191104491090598, "get_state_dump": 0.015235950833275204, "get_robot_state": 0.012626418613252186, "sim_render-ego0": 0.009958596456618536, "get_duckie_state": 3.912335350399925e-06, "in-drivable-lane": 3.3499999999999885, "deviation-heading": 0.7245168989326686, "agent_compute-ego0": 0.09524312927609398, "complete-iteration": 0.40855976740519206, "set_robot_commands": 0.008879264195760092, "deviation-center-line": 0.11362812399728472, "driven_lanedir_consec": 0.6191104491090598, "sim_compute_sim_state": 0.022559284028552826, "sim_compute_performance-ego0": 0.005402117683773949}, "LF-norm-techtrack-000-ego0": {"driven_any": 11.612838119518354, "get_ui_image": 0.05424322994110772, "step_physics": 0.22142767706508412, "survival_time": 29.80000000000029, "driven_lanedir": 7.9295959005599, "get_state_dump": 0.014642582866215026, "get_robot_state": 0.011333888100219932, "sim_render-ego0": 0.00990416336698548, "get_duckie_state": 3.71485698961932e-06, "in-drivable-lane": 7.650000000000109, "deviation-heading": 7.739491893472735, "agent_compute-ego0": 0.09010275484529172, "complete-iteration": 0.44320640452143734, "set_robot_commands": 0.007487141706636004, "deviation-center-line": 1.781083606360325, "driven_lanedir_consec": 7.9295959005599, "sim_compute_sim_state": 0.027778495296561335, "sim_compute_performance-ego0": 0.00609933910657413}, "LF-norm-small_loop-000-ego0": {"driven_any": 24.4406080546598, "get_ui_image": 0.04694003467258069, "step_physics": 0.20011698812568912, "survival_time": 59.99999999999873, "driven_lanedir": 18.24625511555261, "get_state_dump": 0.019349024555863785, "get_robot_state": 0.013819126761227624, "sim_render-ego0": 0.011566025728389284, "get_duckie_state": 3.952269351651131e-06, "in-drivable-lane": 9.29999999999977, "deviation-heading": 21.363012825327445, "agent_compute-ego0": 0.09488862876987378, "complete-iteration": 0.4253058256852835, "set_robot_commands": 0.00986468643074131, "deviation-center-line": 3.82875583598038, "driven_lanedir_consec": 18.24625511555261, "sim_compute_sim_state": 0.021461113604975185, "sim_compute_performance-ego0": 0.007104559802294373}}
set_robot_commands_max0.00986468643074131
set_robot_commands_mean0.00887407444459385
set_robot_commands_median0.009072234820499046
set_robot_commands_min0.007487141706636004
sim_compute_performance-ego0_max0.007131007589170279
sim_compute_performance-ego0_mean0.006434256045453183
sim_compute_performance-ego0_median0.006601949454434252
sim_compute_performance-ego0_min0.005402117683773949
sim_compute_sim_state_max0.027778495296561335
sim_compute_sim_state_mean0.024054912899381553
sim_compute_sim_state_median0.023490021347994845
sim_compute_sim_state_min0.021461113604975185
sim_render-ego0_max0.011566025728389284
sim_render-ego0_mean0.01057197976310868
sim_render-ego0_median0.01040886497852998
sim_render-ego0_min0.00990416336698548
simulation-passed1
step_physics_max0.22142767706508412
step_physics_mean0.19996269567969369
step_physics_median0.20209106070909968
step_physics_min0.17424098423549106
survival_time_max59.99999999999873
survival_time_mean38.74999999999943
survival_time_min5.1999999999999895
No reset possible
6236513511András Kalapos 🇭🇺real-v1.0-3091-310aido-LFP-sim-validation350successyesreg020:06:38
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survival_time_median4.82499999999999
in-drivable-lane_median0.0
driven_lanedir_consec_median1.7807603552476678
deviation-center-line_median0.2125044631694849


other stats
agent_compute-ego0_max0.03423274435648104
agent_compute-ego0_mean0.03228880108893599
agent_compute-ego0_median0.03225643214891667
agent_compute-ego0_min0.03040959570142958
complete-iteration_max0.4904948065920574
complete-iteration_mean0.4420775635350581
complete-iteration_median0.4706707137875853
complete-iteration_min0.33647401997300447
deviation-center-line_max0.25030702338391125
deviation-center-line_mean0.20580105728634104
deviation-center-line_min0.14788827942248295
deviation-heading_max1.0746077097900149
deviation-heading_mean0.8339647427100714
deviation-heading_median0.8497564759944369
deviation-heading_min0.5617383090613964
driven_any_max2.434104267065927
driven_any_mean1.6471846881772971
driven_any_median1.8103105967594055
driven_any_min0.5340132921244504
driven_lanedir_consec_max2.392583861915514
driven_lanedir_consec_mean1.5906307887597428
driven_lanedir_consec_min0.40841858262812114
driven_lanedir_max2.392583861915514
driven_lanedir_mean1.5906307887597428
driven_lanedir_median1.7807603552476678
driven_lanedir_min0.40841858262812114
get_duckie_state_max0.07982875393555228
get_duckie_state_mean0.05495002182747045
get_duckie_state_median0.06398528077738072
get_duckie_state_min0.012000771819568072
get_robot_state_max0.01256347762213813
get_robot_state_mean0.011807057901233676
get_robot_state_median0.011950266404134758
get_robot_state_min0.01076422117452706
get_state_dump_max0.027374895607552876
get_state_dump_mean0.02340002415750089
get_state_dump_median0.02376242574806176
get_state_dump_min0.018700349526327164
get_ui_image_max0.0625195026397705
get_ui_image_mean0.05458469701845128
get_ui_image_median0.05451817411391441
get_ui_image_min0.04678293720620577
in-drivable-lane_max0.20000000000000007
in-drivable-lane_mean0.05000000000000002
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.158436217797806, "get_ui_image": 0.05281918660729333, "step_physics": 0.21832187196849723, "survival_time": 5.599999999999988, "driven_lanedir": 2.122501667991422, "get_state_dump": 0.025186344585587495, "get_robot_state": 0.01076422117452706, "sim_render-ego0": 0.009240551332456876, "get_duckie_state": 0.07982875393555228, "in-drivable-lane": 0.0, "deviation-heading": 1.0276683277994243, "agent_compute-ego0": 0.033938034445838594, "complete-iteration": 0.46576645732980915, "set_robot_commands": 0.0070303541369142785, "deviation-center-line": 0.2447953716748299, "driven_lanedir_consec": 2.122501667991422, "sim_compute_sim_state": 0.021389026557449745, "sim_compute_performance-ego0": 0.007030216993483822}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5340132921244504, "get_ui_image": 0.0625195026397705, "step_physics": 0.23725022739834256, "survival_time": 2.2, "driven_lanedir": 0.40841858262812114, "get_state_dump": 0.022338506910536023, "get_robot_state": 0.01256347762213813, "sim_render-ego0": 0.01214906374613444, "get_duckie_state": 0.06048634847005208, "in-drivable-lane": 0.20000000000000007, "deviation-heading": 0.6718446241894495, "agent_compute-ego0": 0.03040959570142958, "complete-iteration": 0.4755749702453614, "set_robot_commands": 0.009253189298841688, "deviation-center-line": 0.14788827942248295, "driven_lanedir_consec": 0.40841858262812114, "sim_compute_sim_state": 0.019869539472791884, "sim_compute_performance-ego0": 0.00852453973558214}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.462184975721005, "get_ui_image": 0.0562171616205355, "step_physics": 0.24022969676227104, "survival_time": 4.049999999999994, "driven_lanedir": 1.4390190425039138, "get_state_dump": 0.027374895607552876, "get_robot_state": 0.011780203842535251, "sim_render-ego0": 0.010511247123160011, "get_duckie_state": 0.06748421308470935, "in-drivable-lane": 0.0, "deviation-heading": 0.5617383090613964, "agent_compute-ego0": 0.03423274435648104, "complete-iteration": 0.4904948065920574, "set_robot_commands": 0.006079007939594548, "deviation-center-line": 0.1802135546641399, "driven_lanedir_consec": 1.4390190425039138, "sim_compute_sim_state": 0.02979903977091719, "sim_compute_performance-ego0": 0.006563413433912323}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.434104267065927, "get_ui_image": 0.04678293720620577, "step_physics": 0.17565996920476196, "survival_time": 6.0499999999999865, "driven_lanedir": 2.392583861915514, "get_state_dump": 0.018700349526327164, "get_robot_state": 0.012120328965734265, "sim_render-ego0": 0.009067424008103668, "get_duckie_state": 0.012000771819568072, "in-drivable-lane": 0.0, "deviation-heading": 1.0746077097900149, "agent_compute-ego0": 0.03057482985199475, "complete-iteration": 0.33647401997300447, "set_robot_commands": 0.00690889163095443, "deviation-center-line": 0.25030702338391125, "driven_lanedir_consec": 2.392583861915514, "sim_compute_sim_state": 0.015746484037305487, "sim_compute_performance-ego0": 0.008703331478306504}}
set_robot_commands_max0.009253189298841688
set_robot_commands_mean0.007317860751576236
set_robot_commands_median0.0069696228839343545
set_robot_commands_min0.006079007939594548
sim_compute_performance-ego0_max0.008703331478306504
sim_compute_performance-ego0_mean0.007705375410321197
sim_compute_performance-ego0_median0.007777378364532981
sim_compute_performance-ego0_min0.006563413433912323
sim_compute_sim_state_max0.02979903977091719
sim_compute_sim_state_mean0.021701022459616075
sim_compute_sim_state_median0.020629283015120813
sim_compute_sim_state_min0.015746484037305487
sim_render-ego0_max0.01214906374613444
sim_render-ego0_mean0.010242071552463749
sim_render-ego0_median0.009875899227808444
sim_render-ego0_min0.009067424008103668
simulation-passed1
step_physics_max0.24022969676227104
step_physics_mean0.2178654413334682
step_physics_median0.2277860496834199
step_physics_min0.17565996920476196
survival_time_max6.0499999999999865
survival_time_mean4.474999999999992
survival_time_min2.2
No reset possible
6236313518András Kalapos 🇭🇺real-v1.0-3091-310aido-LFV_multi-sim-validation356failedyesreg020:01:55
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego3" aborted with the following error:

error in ego3 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6236213518András Kalapos 🇭🇺real-v1.0-3091-310aido-LFV_multi-sim-validation356failedyesreg020:02:03
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego3" aborted with the following error:

error in ego3 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6236113518András Kalapos 🇭🇺real-v1.0-3091-310aido-LFV_multi-sim-validation356failedyesreg020:02:03
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego2" aborted with the following error:

error in ego2 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
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6235913526András Kalapos 🇭🇺real-v1.0-3092-363aido-LFP-sim-validation350successyesreg020:06:51
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survival_time_median5.04999999999999
in-drivable-lane_median0.0
driven_lanedir_consec_median1.9593204785449303
deviation-center-line_median0.23830090791283623


other stats
agent_compute-ego0_max0.037245718638102214
agent_compute-ego0_mean0.03503768114510519
agent_compute-ego0_median0.03504106052356755
agent_compute-ego0_min0.032822884895183424
complete-iteration_max0.5104097872972488
complete-iteration_mean0.4482669417733056
complete-iteration_median0.4712564550064228
complete-iteration_min0.34014506978312814
deviation-center-line_max0.3528529324499461
deviation-center-line_mean0.25304811796713617
deviation-center-line_min0.18273772359292612
deviation-heading_max0.9015726047438732
deviation-heading_mean0.7552447418226131
deviation-heading_median0.7634416887969286
deviation-heading_min0.592522984952722
driven_any_max2.5756950910974044
driven_any_mean1.7739016714869709
driven_any_median1.9859607253994271
driven_any_min0.5479901440516255
driven_lanedir_consec_max2.549101309173078
driven_lanedir_consec_mean1.7043317400964968
driven_lanedir_consec_min0.3495846941230478
driven_lanedir_max2.549101309173078
driven_lanedir_mean1.7043317400964968
driven_lanedir_median1.9593204785449303
driven_lanedir_min0.3495846941230478
get_duckie_state_max0.08570938419412684
get_duckie_state_mean0.05998241878898212
get_duckie_state_median0.07034056625432439
get_duckie_state_min0.013539158453152874
get_robot_state_max0.012790878613789875
get_robot_state_mean0.012267156236023622
get_robot_state_median0.012259168757332696
get_robot_state_min0.011759408815639224
get_state_dump_max0.02521308483900847
get_state_dump_mean0.02337679630506661
get_state_dump_median0.024908001720905303
get_state_dump_min0.01847809693944736
get_ui_image_max0.06558592584398058
get_ui_image_mean0.057290951660585746
get_ui_image_median0.05798748880624771
get_ui_image_min0.04760290318586695
in-drivable-lane_max0.3500000000000002
in-drivable-lane_mean0.08750000000000005
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.1626001152756587, "get_ui_image": 0.05467542012532552, "step_physics": 0.2040079258106373, "survival_time": 5.349999999999989, "driven_lanedir": 2.1374551883253323, "get_state_dump": 0.02521308483900847, "get_robot_state": 0.012790878613789875, "sim_render-ego0": 0.012308211238295943, "get_duckie_state": 0.08570938419412684, "in-drivable-lane": 0.0, "deviation-heading": 0.8447213492321968, "agent_compute-ego0": 0.032822884895183424, "complete-iteration": 0.4600662235860471, "set_robot_commands": 0.00794298119015164, "deviation-center-line": 0.26490646997124373, "driven_lanedir_consec": 2.1374551883253323, "sim_compute_sim_state": 0.01878176795111762, "sim_compute_performance-ego0": 0.005585321673640498}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5479901440516255, "get_ui_image": 0.06558592584398058, "step_physics": 0.22704813215467665, "survival_time": 2.2, "driven_lanedir": 0.3495846941230478, "get_state_dump": 0.02478028933207194, "get_robot_state": 0.012485053804185657, "sim_render-ego0": 0.009172889921400284, "get_duckie_state": 0.06570533646477593, "in-drivable-lane": 0.3500000000000002, "deviation-heading": 0.592522984952722, "agent_compute-ego0": 0.037245718638102214, "complete-iteration": 0.4824466864267985, "set_robot_commands": 0.008246723810831707, "deviation-center-line": 0.18273772359292612, "driven_lanedir_consec": 0.3495846941230478, "sim_compute_sim_state": 0.023224751154581707, "sim_compute_performance-ego0": 0.008722427156236437}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.809321335523196, "get_ui_image": 0.0612995574871699, "step_physics": 0.2466374039649963, "survival_time": 4.749999999999991, "driven_lanedir": 1.7811857687645285, "get_state_dump": 0.025035714109738667, "get_robot_state": 0.012033283710479736, "sim_render-ego0": 0.009580289324124656, "get_duckie_state": 0.07497579604387283, "in-drivable-lane": 0.0, "deviation-heading": 0.6821620283616605, "agent_compute-ego0": 0.03540161748727163, "complete-iteration": 0.5104097872972488, "set_robot_commands": 0.00914417952299118, "deviation-center-line": 0.2116953458544288, "driven_lanedir_consec": 1.7811857687645285, "sim_compute_sim_state": 0.029024221003055573, "sim_compute_performance-ego0": 0.007052679856618245}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.5756950910974044, "get_ui_image": 0.04760290318586695, "step_physics": 0.17255732956833728, "survival_time": 6.299999999999986, "driven_lanedir": 2.549101309173078, "get_state_dump": 0.01847809693944736, "get_robot_state": 0.011759408815639224, "sim_render-ego0": 0.010691229752668244, "get_duckie_state": 0.013539158453152874, "in-drivable-lane": 0.0, "deviation-heading": 0.9015726047438732, "agent_compute-ego0": 0.034680503559863476, "complete-iteration": 0.34014506978312814, "set_robot_commands": 0.008635734948586292, "deviation-center-line": 0.3528529324499461, "driven_lanedir_consec": 2.549101309173078, "sim_compute_sim_state": 0.015632295233058178, "sim_compute_performance-ego0": 0.006348731949573427}}
set_robot_commands_max0.00914417952299118
set_robot_commands_mean0.008492404868140204
set_robot_commands_median0.008441229379708998
set_robot_commands_min0.00794298119015164
sim_compute_performance-ego0_max0.008722427156236437
sim_compute_performance-ego0_mean0.006927290159017152
sim_compute_performance-ego0_median0.0067007059030958355
sim_compute_performance-ego0_min0.005585321673640498
sim_compute_sim_state_max0.029024221003055573
sim_compute_sim_state_mean0.02166575883545327
sim_compute_sim_state_median0.021003259552849665
sim_compute_sim_state_min0.015632295233058178
sim_render-ego0_max0.012308211238295943
sim_render-ego0_mean0.01043815505912228
sim_render-ego0_median0.01013575953839645
sim_render-ego0_min0.009172889921400284
simulation-passed1
step_physics_max0.2466374039649963
step_physics_mean0.2125626978746619
step_physics_median0.21552802898265697
step_physics_min0.17255732956833728
survival_time_max6.299999999999986
survival_time_mean4.6499999999999915
survival_time_min2.2
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6235213543András Kalapos 🇭🇺3090aido-LFV-sim-validation354successyesreg020:33:47
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survival_time_median9.125000000000002
in-drivable-lane_median0.0
driven_lanedir_consec_median3.7774444823176174
deviation-center-line_median0.38905462673303226


other stats
agent_compute-ego0_max0.03597261596890265
agent_compute-ego0_mean0.03444380947989982
agent_compute-ego0_median0.034884395433420486
agent_compute-ego0_min0.032033831083855664
agent_compute-npc0_max0.08070842911597012
agent_compute-npc0_mean0.0707315090641091
agent_compute-npc0_median0.06999670077856615
agent_compute-npc0_min0.06222420558333397
complete-iteration_max1.885001434135842
complete-iteration_mean1.226869231097848
complete-iteration_median1.215947299218871
complete-iteration_min0.5905808918178082
deviation-center-line_max0.9419496488367314
deviation-center-line_mean0.4849446024904949
deviation-center-line_min0.21971950765918372
deviation-heading_max3.851988618657838
deviation-heading_mean1.9539208285368872
deviation-heading_median1.56366551162257
deviation-heading_min0.8363636722445723
driven_any_max10.537812964017052
driven_any_mean5.193516423753788
driven_any_median3.863845700866004
driven_any_min2.508561329266092
driven_lanedir_consec_max10.329569983640722
driven_lanedir_consec_mean5.090985952229395
driven_lanedir_consec_min2.4794848606416227
driven_lanedir_max10.329569983640722
driven_lanedir_mean5.090985952229395
driven_lanedir_median3.7774444823176174
driven_lanedir_min2.4794848606416227
get_duckie_state_max3.871050747958097e-06
get_duckie_state_mean3.5959206717497896e-06
get_duckie_state_median3.534300529056511e-06
get_duckie_state_min3.4440308809280396e-06
get_robot_state_max0.06628738455220956
get_robot_state_mean0.050876415758595125
get_robot_state_median0.0583606691209535
get_robot_state_min0.02049694024026394
get_state_dump_max0.034965328618782716
get_state_dump_mean0.03053344856861216
get_state_dump_median0.029752550324609536
get_state_dump_min0.02766336500644684
get_ui_image_max0.092848172359912
get_ui_image_mean0.07805844528764416
get_ui_image_median0.08226178015686511
get_ui_image_min0.05486204847693443
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LFV-norm-loop-000-ego0": {"driven_any": 4.655485786475105, "get_ui_image": 0.07599395730278709, "step_physics": 0.46224250793457033, "survival_time": 10.95000000000002, "driven_lanedir": 4.518307794054724, "get_state_dump": 0.02795258543708108, "get_robot_state": 0.05183791680769487, "sim_render-ego0": 0.01353457190773704, "sim_render-npc0": 0.011351552876559172, "sim_render-npc1": 0.011054586280475964, "sim_render-npc2": 0.011278256503018466, "get_duckie_state": 3.871050747958097e-06, "in-drivable-lane": 0.0, "deviation-heading": 2.1258062121455565, "agent_compute-ego0": 0.03537544120441784, "agent_compute-npc0": 0.07097339955243197, "agent_compute-npc1": 0.07121495116840709, "agent_compute-npc2": 0.08680193640969017, "complete-iteration": 1.0391601649197666, "set_robot_commands": 0.008502364158630371, "deviation-center-line": 0.4656747842534762, "driven_lanedir_consec": 4.518307794054724, "sim_compute_sim_state": 0.04831180789253928, "sim_compute_performance-ego0": 0.0063727519728920675, "sim_compute_performance-npc0": 0.0066992402076721195, "sim_compute_performance-npc1": 0.006160469488664107, "sim_compute_performance-npc2": 0.006441969221288508}, "LFV-norm-zigzag-000-ego0": {"driven_any": 10.537812964017052, "get_ui_image": 0.092848172359912, "step_physics": 1.1405500656986438, "survival_time": 23.5000000000002, "driven_lanedir": 10.329569983640722, "get_state_dump": 0.03155251521213799, "get_robot_state": 0.06488342143421214, "sim_render-ego0": 0.010848929674508971, "sim_render-npc0": 0.010409623953946836, "sim_render-npc1": 0.010925166925806909, "sim_render-npc2": 0.010506238653907856, "sim_render-npc3": 0.010453736453046212, "get_duckie_state": 3.5945017626331113e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.851988618657838, "agent_compute-ego0": 0.03597261596890265, "agent_compute-npc0": 0.06902000200470035, "agent_compute-npc1": 0.07194677431871936, "agent_compute-npc2": 0.07485081334529156, "agent_compute-npc3": 0.0698170545501061, "complete-iteration": 1.885001434135842, "set_robot_commands": 0.00838162286519498, "deviation-center-line": 0.9419496488367314, "driven_lanedir_consec": 10.329569983640722, "sim_compute_sim_state": 0.10544544876001444, "sim_compute_performance-ego0": 0.006815848077178761, "sim_compute_performance-npc0": 0.007479064277276365, "sim_compute_performance-npc1": 0.0065976550877727014, "sim_compute_performance-npc2": 0.006475285613106568, "sim_compute_performance-npc3": 0.006837637814240344}, "LFV-norm-techtrack-000-ego0": {"driven_any": 3.072205615256902, "get_ui_image": 0.08852960301094315, "step_physics": 0.6216416310290901, "survival_time": 7.299999999999982, "driven_lanedir": 3.0365811705805106, "get_state_dump": 0.034965328618782716, "get_robot_state": 0.06628738455220956, "sim_render-ego0": 0.00962151151125123, "sim_render-npc0": 0.011350059184898323, "sim_render-npc1": 0.010589406603858584, "sim_render-npc2": 0.013634331372319435, "sim_render-npc3": 0.011664693858347782, "get_duckie_state": 3.4740992954799107e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.0015248110995834, "agent_compute-ego0": 0.032033831083855664, "agent_compute-npc0": 0.08070842911597012, "agent_compute-npc1": 0.0819012924116485, "agent_compute-npc2": 0.07617696775060122, "agent_compute-npc3": 0.0826807638414863, "complete-iteration": 1.392734433517975, "set_robot_commands": 0.00843611055490922, "deviation-center-line": 0.31243446921258833, "driven_lanedir_consec": 3.0365811705805106, "sim_compute_sim_state": 0.0949022948336439, "sim_compute_performance-ego0": 0.006971956110324989, "sim_compute_performance-npc0": 0.006794577553158715, "sim_compute_performance-npc1": 0.006700038909912109, "sim_compute_performance-npc2": 0.006123095142598055, "sim_compute_performance-npc3": 0.006233836517852991}, "LFV-norm-small_loop-000-ego0": {"driven_any": 2.508561329266092, "get_ui_image": 0.05486204847693443, "step_physics": 0.3170172553509474, "survival_time": 6.349999999999985, "driven_lanedir": 2.4794848606416227, "get_state_dump": 0.02766336500644684, "get_robot_state": 0.02049694024026394, "sim_render-ego0": 0.010109128430485724, "sim_render-npc0": 0.009894292801618576, "get_duckie_state": 3.4440308809280396e-06, "in-drivable-lane": 0.0, "deviation-heading": 0.8363636722445723, "agent_compute-ego0": 0.034393349662423134, "agent_compute-npc0": 0.06222420558333397, "complete-iteration": 0.5905808918178082, "set_robot_commands": 0.008094081655144691, "deviation-center-line": 0.21971950765918372, "driven_lanedir_consec": 2.4794848606416227, "sim_compute_sim_state": 0.024923795834183693, "sim_compute_performance-ego0": 0.005902526900172234, "sim_compute_performance-npc0": 0.006662817671895027}}
set_robot_commands_max0.008502364158630371
set_robot_commands_mean0.008353544808469816
set_robot_commands_median0.0084088667100521
set_robot_commands_min0.008094081655144691
sim_compute_performance-ego0_max0.006971956110324989
sim_compute_performance-ego0_mean0.006515770765142013
sim_compute_performance-ego0_median0.006594300025035414
sim_compute_performance-ego0_min0.005902526900172234
sim_compute_performance-npc0_max0.007479064277276365
sim_compute_performance-npc0_mean0.006908924927500556
sim_compute_performance-npc0_median0.006746908880415417
sim_compute_performance-npc0_min0.006662817671895027
sim_compute_sim_state_max0.10544544876001444
sim_compute_sim_state_mean0.06839583683009533
sim_compute_sim_state_median0.07160705136309159
sim_compute_sim_state_min0.024923795834183693
sim_render-ego0_max0.01353457190773704
sim_render-ego0_mean0.01102853538099574
sim_render-ego0_median0.010479029052497348
sim_render-ego0_min0.00962151151125123
sim_render-npc0_max0.011351552876559172
sim_render-npc0_mean0.010751382204255727
sim_render-npc0_median0.01087984156942258
sim_render-npc0_min0.009894292801618576
simulation-passed1
step_physics_max1.1405500656986438
step_physics_mean0.6353628650033128
step_physics_median0.5419420694818302
step_physics_min0.3170172553509474
survival_time_max23.5000000000002
survival_time_mean12.025000000000048
survival_time_min6.349999999999985
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driven_lanedir_consec_median28.84619913006924
survival_time_median59.99999999999873
deviation-center-line_median3.970203078397303
in-drivable-lane_median1.4749999999999628


other stats
agent_compute-ego0_max0.1341736217422549
agent_compute-ego0_mean0.12578007397703286
agent_compute-ego0_median0.12349254146007375
agent_compute-ego0_min0.12196159124572908
complete-iteration_max0.5720763921142121
complete-iteration_mean0.4877136431566186
complete-iteration_median0.47778683667575983
complete-iteration_min0.4232045071607426
deviation-center-line_max4.127710224362671
deviation-center-line_mean3.945267088382845
deviation-center-line_min3.7129519723740994
deviation-heading_max10.501109418167191
deviation-heading_mean9.145659784394956
deviation-heading_median9.306303549683657
deviation-heading_min7.468922620045325
driven_any_max30.929537302909985
driven_any_mean29.555792723201492
driven_any_median29.87079644146479
driven_any_min27.5520407069664
driven_lanedir_consec_max30.346691393765976
driven_lanedir_consec_mean28.51160341809545
driven_lanedir_consec_min26.007324018477345
driven_lanedir_max30.346691393765976
driven_lanedir_mean28.51160341809545
driven_lanedir_median28.84619913006924
driven_lanedir_min26.007324018477345
get_duckie_state_max3.265600021832392e-06
get_duckie_state_mean3.095868227384569e-06
get_duckie_state_median3.105198513161233e-06
get_duckie_state_min2.9074758613834176e-06
get_robot_state_max0.014628841319151663
get_robot_state_mean0.012852531289379365
get_robot_state_median0.012748646894958394
get_robot_state_min0.011283990048449006
get_state_dump_max0.017818061437932377
get_state_dump_mean0.01636139295381074
get_state_dump_median0.016399284882112706
get_state_dump_min0.014828940613085186
get_ui_image_max0.06253422964224709
get_ui_image_mean0.05440576378252981
get_ui_image_median0.053502009969071285
get_ui_image_min0.048084805549729576
in-drivable-lane_max2.599999999999927
in-drivable-lane_mean1.387499999999963
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 30.929537302909985, "get_ui_image": 0.04875574084146136, "step_physics": 0.18003123269093027, "survival_time": 59.99999999999873, "driven_lanedir": 30.346691393765976, "get_state_dump": 0.016594274355708115, "get_robot_state": 0.014628841319151663, "sim_render-ego0": 0.011207368550550729, "get_duckie_state": 3.265600021832392e-06, "in-drivable-lane": 0.4999999999999858, "deviation-heading": 7.468922620045325, "agent_compute-ego0": 0.12196159124572908, "complete-iteration": 0.43121189042789354, "set_robot_commands": 0.006461008899317097, "deviation-center-line": 3.7129519723740994, "driven_lanedir_consec": 30.346691393765976, "sim_compute_sim_state": 0.024034436596720343, "sim_compute_performance-ego0": 0.007338520886995314}, "LF-norm-zigzag-000-ego0": {"driven_any": 27.5520407069664, "get_ui_image": 0.06253422964224709, "step_physics": 0.2984433849090144, "survival_time": 59.99999999999873, "driven_lanedir": 26.007324018477345, "get_state_dump": 0.017818061437932377, "get_robot_state": 0.01294851739837367, "sim_render-ego0": 0.011096315121869064, "get_duckie_state": 3.0924934431674775e-06, "in-drivable-lane": 2.44999999999994, "deviation-heading": 10.501109418167191, "agent_compute-ego0": 0.12329137414619389, "complete-iteration": 0.5720763921142121, "set_robot_commands": 0.007016805486814068, "deviation-center-line": 3.9997216720454754, "driven_lanedir_consec": 26.007324018477345, "sim_compute_sim_state": 0.03171412176534, "sim_compute_performance-ego0": 0.00701269380853734}, "LF-norm-techtrack-000-ego0": {"driven_any": 29.7144936538206, "get_ui_image": 0.05824827909668121, "step_physics": 0.25343568140422174, "survival_time": 59.99999999999873, "driven_lanedir": 28.258434951851065, "get_state_dump": 0.014828940613085186, "get_robot_state": 0.011283990048449006, "sim_render-ego0": 0.009954556934442449, "get_duckie_state": 2.9074758613834176e-06, "in-drivable-lane": 2.599999999999927, "deviation-heading": 8.742173693703313, "agent_compute-ego0": 0.1341736217422549, "complete-iteration": 0.5243617829236261, "set_robot_commands": 0.0063958233540302315, "deviation-center-line": 3.9406844847491302, "driven_lanedir_consec": 28.258434951851065, "sim_compute_sim_state": 0.02910862119866847, "sim_compute_performance-ego0": 0.0067445791134925605}, "LF-norm-small_loop-000-ego0": {"driven_any": 30.02709922910898, "get_ui_image": 0.048084805549729576, "step_physics": 0.17991989895664187, "survival_time": 59.99999999999873, "driven_lanedir": 29.43396330828741, "get_state_dump": 0.016204295408517297, "get_robot_state": 0.012548776391543118, "sim_render-ego0": 0.01059615066109053, "get_duckie_state": 3.117903583154988e-06, "in-drivable-lane": 0.0, "deviation-heading": 9.870433405663997, "agent_compute-ego0": 0.12369370877395364, "complete-iteration": 0.4232045071607426, "set_robot_commands": 0.006630010549273717, "deviation-center-line": 4.127710224362671, "driven_lanedir_consec": 29.43396330828741, "sim_compute_sim_state": 0.017912273899303884, "sim_compute_performance-ego0": 0.007421195953712177}}
set_robot_commands_max0.007016805486814068
set_robot_commands_mean0.006625912072358779
set_robot_commands_median0.006545509724295407
set_robot_commands_min0.0063958233540302315
sim_compute_performance-ego0_max0.007421195953712177
sim_compute_performance-ego0_mean0.0071292474406843475
sim_compute_performance-ego0_median0.007175607347766327
sim_compute_performance-ego0_min0.0067445791134925605
sim_compute_sim_state_max0.03171412176534
sim_compute_sim_state_mean0.025692363365008176
sim_compute_sim_state_median0.026571528897694405
sim_compute_sim_state_min0.017912273899303884
sim_render-ego0_max0.011207368550550729
sim_render-ego0_mean0.010713597816988194
sim_render-ego0_median0.010846232891479795
sim_render-ego0_min0.009954556934442449
simulation-passed1
step_physics_max0.2984433849090144
step_physics_mean0.22795754949020203
step_physics_median0.216733457047576
step_physics_min0.17991989895664187
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
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6233813586Andras Beres202-1aido-LFP-sim-validation350successyesreg020:07:11
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survival_time_median4.674999999999992
in-drivable-lane_median0.0
driven_lanedir_consec_median1.8108358619333904
deviation-center-line_median0.29566379245731533


other stats
agent_compute-ego0_max0.13346629447125374
agent_compute-ego0_mean0.1258509320386655
agent_compute-ego0_median0.12526798380840798
agent_compute-ego0_min0.1194014660665922
complete-iteration_max0.6308442125929162
complete-iteration_mean0.5464685857326619
complete-iteration_median0.5510884628824148
complete-iteration_min0.452853204572902
deviation-center-line_max0.4257199605729894
deviation-center-line_mean0.3022154764963995
deviation-center-line_min0.19181436049797795
deviation-heading_max1.214770654593495
deviation-heading_mean0.7660048699036226
deviation-heading_median0.7328560647892886
deviation-heading_min0.38353669544241825
driven_any_max2.646667534116804
driven_any_mean1.7157455667398422
driven_any_median1.837712427899878
driven_any_min0.540889877042809
driven_lanedir_consec_max2.593616185382574
driven_lanedir_consec_mean1.6873832356985865
driven_lanedir_consec_min0.53424503354499
driven_lanedir_max2.593616185382574
driven_lanedir_mean1.6873832356985865
driven_lanedir_median1.8108358619333904
driven_lanedir_min0.53424503354499
get_duckie_state_max0.08401360244394462
get_duckie_state_mean0.06190557807199842
get_duckie_state_median0.07474002206517305
get_duckie_state_min0.01412866571370293
get_robot_state_max0.012468317721752413
get_robot_state_mean0.011453207440705226
get_robot_state_median0.01167541895854003
get_robot_state_min0.009993674123988432
get_state_dump_max0.02474789084675156
get_state_dump_mean0.02154600309595405
get_state_dump_median0.022128656297752018
get_state_dump_min0.017178808941560632
get_ui_image_max0.07041559320815066
get_ui_image_mean0.05885441948531851
get_ui_image_median0.059644524616887525
get_ui_image_min0.04571303549934836
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.215345931084566, "get_ui_image": 0.05674900072757329, "step_physics": 0.18245203918385727, "survival_time": 5.299999999999989, "driven_lanedir": 2.192827296192858, "get_state_dump": 0.02474789084675156, "get_robot_state": 0.012276725234272324, "sim_render-ego0": 0.010412403356249088, "get_duckie_state": 0.08401360244394462, "in-drivable-lane": 0.0, "deviation-heading": 0.6869131618596741, "agent_compute-ego0": 0.1194014660665922, "complete-iteration": 0.5230597090498309, "set_robot_commands": 0.006231784820556641, "deviation-center-line": 0.31399878214877835, "driven_lanedir_consec": 2.192827296192858, "sim_compute_sim_state": 0.019443075233530775, "sim_compute_performance-ego0": 0.007129261426836531}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.540889877042809, "get_ui_image": 0.07041559320815066, "step_physics": 0.26032102868912066, "survival_time": 2.3, "driven_lanedir": 0.53424503354499, "get_state_dump": 0.021533626191159512, "get_robot_state": 0.012468317721752413, "sim_render-ego0": 0.012120622269650725, "get_duckie_state": 0.07962250202260118, "in-drivable-lane": 0.0, "deviation-heading": 0.38353669544241825, "agent_compute-ego0": 0.13346629447125374, "complete-iteration": 0.6308442125929162, "set_robot_commands": 0.005408941431248442, "deviation-center-line": 0.19181436049797795, "driven_lanedir_consec": 0.53424503354499, "sim_compute_sim_state": 0.026245107042028547, "sim_compute_performance-ego0": 0.009026593350349587}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.4600789247151902, "get_ui_image": 0.06254004850620176, "step_physics": 0.24299076126842964, "survival_time": 4.049999999999994, "driven_lanedir": 1.428844427673923, "get_state_dump": 0.022723686404344513, "get_robot_state": 0.011074112682807736, "sim_render-ego0": 0.01028843623835866, "get_duckie_state": 0.06985754210774492, "in-drivable-lane": 0.0, "deviation-heading": 0.778798967718903, "agent_compute-ego0": 0.12014060776408124, "complete-iteration": 0.5791172167149986, "set_robot_commands": 0.006133954699446515, "deviation-center-line": 0.27732880276585226, "driven_lanedir_consec": 1.428844427673923, "sim_compute_sim_state": 0.026843085521604956, "sim_compute_performance-ego0": 0.0063269777995784105}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.646667534116804, "get_ui_image": 0.04571303549934836, "step_physics": 0.19608478335773244, "survival_time": 6.749999999999984, "driven_lanedir": 2.593616185382574, "get_state_dump": 0.017178808941560632, "get_robot_state": 0.009993674123988432, "sim_render-ego0": 0.010386293425279506, "get_duckie_state": 0.01412866571370293, "in-drivable-lane": 0.0, "deviation-heading": 1.214770654593495, "agent_compute-ego0": 0.13039535985273473, "complete-iteration": 0.452853204572902, "set_robot_commands": 0.007047332385007073, "deviation-center-line": 0.4257199605729894, "driven_lanedir_consec": 2.593616185382574, "sim_compute_sim_state": 0.015485502341214348, "sim_compute_performance-ego0": 0.006247527459088494}}
set_robot_commands_max0.007047332385007073
set_robot_commands_mean0.006205503334064668
set_robot_commands_median0.006182869760001578
set_robot_commands_min0.005408941431248442
sim_compute_performance-ego0_max0.009026593350349587
sim_compute_performance-ego0_mean0.007182590008963256
sim_compute_performance-ego0_median0.00672811961320747
sim_compute_performance-ego0_min0.006247527459088494
sim_compute_sim_state_max0.026843085521604956
sim_compute_sim_state_mean0.022004192534594656
sim_compute_sim_state_median0.02284409113777966
sim_compute_sim_state_min0.015485502341214348
sim_render-ego0_max0.012120622269650725
sim_render-ego0_mean0.010801938822384494
sim_render-ego0_median0.010399348390764295
sim_render-ego0_min0.01028843623835866
simulation-passed1
step_physics_max0.26032102868912066
step_physics_mean0.220462153124785
step_physics_median0.21953777231308105
step_physics_min0.18245203918385727
survival_time_max6.749999999999984
survival_time_mean4.5999999999999925
survival_time_min2.3
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6233113609Andras Beresfsf+ilaido-LFV_multi-sim-validation356successyesreg021:26:11
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survival_time_median25.500000000000227
in-drivable-lane_median0.1750000000000016
driven_lanedir_consec_median5.365662933258638
deviation-center-line_median1.6044755420254524


other stats
agent_compute-ego0_max0.15334198535134216
agent_compute-ego0_mean0.13865371213281324
agent_compute-ego0_median0.12998782072048598
agent_compute-ego0_min0.127238673708273
agent_compute-ego1_max0.1287445092459448
agent_compute-ego1_mean0.11758881268062071
agent_compute-ego1_median0.1126695533088042
agent_compute-ego1_min0.1089144919148858
complete-iteration_max1.714555342744568
complete-iteration_mean1.440327546104495
complete-iteration_median1.5709364087614297
complete-iteration_min0.7957370503348593
deviation-center-line_max4.685258885331869
deviation-center-line_mean2.0940919445811645
deviation-center-line_min0.46580390692958307
deviation-heading_max14.88277701798551
deviation-heading_mean5.198791364535423
deviation-heading_median3.4507574786886765
deviation-heading_min1.1525089054371125
driven_any_max22.4584202936406
driven_any_mean7.7383225525928925
driven_any_median5.426336045563694
driven_any_min1.008631692321679
driven_lanedir_consec_max21.08988072347855
driven_lanedir_consec_mean7.3657621018325345
driven_lanedir_consec_min0.996662061061377
driven_lanedir_max21.08988072347855
driven_lanedir_mean7.3657621018325345
driven_lanedir_median5.365662933258638
driven_lanedir_min0.996662061061377
get_duckie_state_max3.2557433365375793e-06
get_duckie_state_mean3.074806194187998e-06
get_duckie_state_median3.1251897445472725e-06
get_duckie_state_min2.7040517108040566e-06
get_robot_state_max0.05509491861942443
get_robot_state_mean0.04687981683121468
get_robot_state_median0.05017171148694317
get_robot_state_min0.02428164541351129
get_state_dump_max0.03848780277403683
get_state_dump_mean0.031156533004562396
get_state_dump_median0.03094858989537319
get_state_dump_min0.020449155606098057
get_ui_image_max0.08468200484540232
get_ui_image_mean0.0775713991071053
get_ui_image_median0.08082133729863773
get_ui_image_min0.057040678047985766
in-drivable-lane_max7.0000000000000995
in-drivable-lane_mean1.07142857142858
in-drivable-lane_min0.0
per-episodes
details{"LFV_multi-norm-loop-000-ego0": {"driven_any": 1.008631692321679, "get_ui_image": 0.07747621570683559, "step_physics": 0.4975988184932337, "survival_time": 13.80000000000006, "driven_lanedir": 0.996662061061377, "get_state_dump": 0.03848780277403683, "get_robot_state": 0.05509491861942443, "sim_render-ego0": 0.01224734722922425, "sim_render-ego1": 0.011508578427862176, "sim_render-ego2": 0.010813785373949402, "sim_render-ego3": 0.009021975073143031, "get_duckie_state": 3.0288627431711133e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.545648223635081, "agent_compute-ego0": 0.15334198535134216, "agent_compute-ego1": 0.1287445092459448, "agent_compute-ego2": 0.12819374081029788, "agent_compute-ego3": 0.12682458467862237, "complete-iteration": 1.3577861346923057, "set_robot_commands": 0.0099511378938971, "deviation-center-line": 1.660288615147687, "driven_lanedir_consec": 0.996662061061377, "sim_compute_sim_state": 0.04215823011708174, "sim_compute_performance-ego0": 0.007430179024431249, "sim_compute_performance-ego1": 0.006208084120216783, "sim_compute_performance-ego2": 0.006029762515952871, "sim_compute_performance-ego3": 0.005483917380928563}, "LFV_multi-norm-loop-000-ego1": {"driven_any": 3.2709867692247396, "get_ui_image": 0.07747621570683559, "step_physics": 0.4975988184932337, "survival_time": 13.80000000000006, "driven_lanedir": 3.2464012273197382, "get_state_dump": 0.03848780277403683, "get_robot_state": 0.05509491861942443, "sim_render-ego0": 0.01224734722922425, "sim_render-ego1": 0.011508578427862176, "sim_render-ego2": 0.010813785373949402, "sim_render-ego3": 0.009021975073143031, "get_duckie_state": 3.0288627431711133e-06, "in-drivable-lane": 0.0, "deviation-heading": 2.932839420396196, "agent_compute-ego0": 0.15334198535134216, "agent_compute-ego1": 0.1287445092459448, "agent_compute-ego2": 0.12819374081029788, "agent_compute-ego3": 0.12682458467862237, "complete-iteration": 1.3577861346923057, "set_robot_commands": 0.0099511378938971, "deviation-center-line": 0.525396175196958, "driven_lanedir_consec": 3.2464012273197382, "sim_compute_sim_state": 0.04215823011708174, "sim_compute_performance-ego0": 0.007430179024431249, "sim_compute_performance-ego1": 0.006208084120216783, "sim_compute_performance-ego2": 0.006029762515952871, "sim_compute_performance-ego3": 0.005483917380928563}, "LFV_multi-norm-loop-000-ego2": {"driven_any": 1.1193219752650816, "get_ui_image": 0.07747621570683559, "step_physics": 0.4975988184932337, "survival_time": 13.80000000000006, "driven_lanedir": 1.1137738202113483, "get_state_dump": 0.03848780277403683, "get_robot_state": 0.05509491861942443, "sim_render-ego0": 0.01224734722922425, "sim_render-ego1": 0.011508578427862176, "sim_render-ego2": 0.010813785373949402, "sim_render-ego3": 0.009021975073143031, "get_duckie_state": 3.0288627431711133e-06, "in-drivable-lane": 0.0, "deviation-heading": 2.945834407431119, "agent_compute-ego0": 0.15334198535134216, "agent_compute-ego1": 0.1287445092459448, "agent_compute-ego2": 0.12819374081029788, "agent_compute-ego3": 0.12682458467862237, "complete-iteration": 1.3577861346923057, "set_robot_commands": 0.0099511378938971, "deviation-center-line": 0.7126768326024951, "driven_lanedir_consec": 1.1137738202113483, "sim_compute_sim_state": 0.04215823011708174, "sim_compute_performance-ego0": 0.007430179024431249, "sim_compute_performance-ego1": 0.006208084120216783, "sim_compute_performance-ego2": 0.006029762515952871, "sim_compute_performance-ego3": 0.005483917380928563}, "LFV_multi-norm-loop-000-ego3": {"driven_any": 6.720676979656855, "get_ui_image": 0.07747621570683559, "step_physics": 0.4975988184932337, "survival_time": 13.80000000000006, "driven_lanedir": 6.648836904221607, "get_state_dump": 0.03848780277403683, "get_robot_state": 0.05509491861942443, "sim_render-ego0": 0.01224734722922425, "sim_render-ego1": 0.011508578427862176, "sim_render-ego2": 0.010813785373949402, "sim_render-ego3": 0.009021975073143031, "get_duckie_state": 3.0288627431711133e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.6814782631023315, "agent_compute-ego0": 0.15334198535134216, "agent_compute-ego1": 0.1287445092459448, "agent_compute-ego2": 0.12819374081029788, "agent_compute-ego3": 0.12682458467862237, "complete-iteration": 1.3577861346923057, "set_robot_commands": 0.0099511378938971, "deviation-center-line": 0.8396215759798198, "driven_lanedir_consec": 6.648836904221607, "sim_compute_sim_state": 0.04215823011708174, "sim_compute_performance-ego0": 0.007430179024431249, "sim_compute_performance-ego1": 0.006208084120216783, "sim_compute_performance-ego2": 0.006029762515952871, "sim_compute_performance-ego3": 0.005483917380928563}, "LFV_multi-norm-zigzag-000-ego0": {"driven_any": 22.4584202936406, "get_ui_image": 0.08468200484540232, "step_physics": 0.9483486447378854, "survival_time": 48.099999999999405, "driven_lanedir": 21.08988072347855, "get_state_dump": 0.03094858989537319, "get_robot_state": 0.05017171148694317, "sim_render-ego0": 0.010154867221757012, "sim_render-ego1": 0.009164111512719904, "sim_render-ego2": 0.009675650829342916, "sim_render-ego3": 0.008930302607059975, "get_duckie_state": 3.1251897445472725e-06, "in-drivable-lane": 2.599999999999987, "deviation-heading": 7.488414182630852, "agent_compute-ego0": 0.127238673708273, "agent_compute-ego1": 0.1089144919148858, "agent_compute-ego2": 0.1066741616555092, "agent_compute-ego3": 0.10511025461452402, "complete-iteration": 1.714555342744568, "set_robot_commands": 0.007177197920818072, "deviation-center-line": 3.5117593562722984, "driven_lanedir_consec": 21.08988072347855, "sim_compute_sim_state": 0.06619628245709222, "sim_compute_performance-ego0": 0.006809965224776065, "sim_compute_performance-ego1": 0.004584217616207008, "sim_compute_performance-ego2": 0.004622751802422671, "sim_compute_performance-ego3": 0.005011652365156671}, "LFV_multi-norm-zigzag-000-ego1": {"driven_any": 11.600709578828434, "get_ui_image": 0.08468200484540232, "step_physics": 0.9483486447378854, "survival_time": 48.099999999999405, "driven_lanedir": 10.92528890466095, "get_state_dump": 0.03094858989537319, "get_robot_state": 0.05017171148694317, "sim_render-ego0": 0.010154867221757012, "sim_render-ego1": 0.009164111512719904, "sim_render-ego2": 0.009675650829342916, "sim_render-ego3": 0.008930302607059975, "get_duckie_state": 3.1251897445472725e-06, "in-drivable-lane": 1.2500000000000044, "deviation-heading": 14.88277701798551, "agent_compute-ego0": 0.127238673708273, "agent_compute-ego1": 0.1089144919148858, "agent_compute-ego2": 0.1066741616555092, "agent_compute-ego3": 0.10511025461452402, "complete-iteration": 1.714555342744568, "set_robot_commands": 0.007177197920818072, "deviation-center-line": 4.685258885331869, "driven_lanedir_consec": 10.92528890466095, "sim_compute_sim_state": 0.06619628245709222, "sim_compute_performance-ego0": 0.006809965224776065, "sim_compute_performance-ego1": 0.004584217616207008, "sim_compute_performance-ego2": 0.004622751802422671, "sim_compute_performance-ego3": 0.005011652365156671}, "LFV_multi-norm-zigzag-000-ego2": {"driven_any": 14.882625929480804, "get_ui_image": 0.08468200484540232, "step_physics": 0.9483486447378854, "survival_time": 48.099999999999405, "driven_lanedir": 14.082195873383895, "get_state_dump": 0.03094858989537319, "get_robot_state": 0.05017171148694317, "sim_render-ego0": 0.010154867221757012, "sim_render-ego1": 0.009164111512719904, "sim_render-ego2": 0.009675650829342916, "sim_render-ego3": 0.008930302607059975, "get_duckie_state": 3.1251897445472725e-06, "in-drivable-lane": 1.4500000000000108, "deviation-heading": 6.631339641284122, "agent_compute-ego0": 0.127238673708273, "agent_compute-ego1": 0.1089144919148858, "agent_compute-ego2": 0.1066741616555092, "agent_compute-ego3": 0.10511025461452402, "complete-iteration": 1.714555342744568, "set_robot_commands": 0.007177197920818072, "deviation-center-line": 3.3761229140722087, "driven_lanedir_consec": 14.082195873383895, "sim_compute_sim_state": 0.06619628245709222, "sim_compute_performance-ego0": 0.006809965224776065, "sim_compute_performance-ego1": 0.004584217616207008, "sim_compute_performance-ego2": 0.004622751802422671, "sim_compute_performance-ego3": 0.005011652365156671}, "LFV_multi-norm-zigzag-000-ego3": {"driven_any": 11.288432337203725, "get_ui_image": 0.08468200484540232, "step_physics": 0.9483486447378854, "survival_time": 48.099999999999405, "driven_lanedir": 10.909233457179283, "get_state_dump": 0.03094858989537319, "get_robot_state": 0.05017171148694317, "sim_render-ego0": 0.010154867221757012, "sim_render-ego1": 0.009164111512719904, "sim_render-ego2": 0.009675650829342916, "sim_render-ego3": 0.008930302607059975, "get_duckie_state": 3.1251897445472725e-06, "in-drivable-lane": 0.3500000000000032, "deviation-heading": 10.291517953394273, "agent_compute-ego0": 0.127238673708273, "agent_compute-ego1": 0.1089144919148858, "agent_compute-ego2": 0.1066741616555092, "agent_compute-ego3": 0.10511025461452402, "complete-iteration": 1.714555342744568, "set_robot_commands": 0.007177197920818072, "deviation-center-line": 3.973005565851766, "driven_lanedir_consec": 10.909233457179283, "sim_compute_sim_state": 0.06619628245709222, "sim_compute_performance-ego0": 0.006809965224776065, "sim_compute_performance-ego1": 0.004584217616207008, "sim_compute_performance-ego2": 0.004622751802422671, "sim_compute_performance-ego3": 0.005011652365156671}, "LFV_multi-norm-techtrack-000-ego0": {"driven_any": 9.289474282206614, "get_ui_image": 0.08082133729863773, "step_physics": 0.8119632530585661, "survival_time": 25.500000000000227, "driven_lanedir": 8.76615241756709, "get_state_dump": 0.02938689504350935, "get_robot_state": 0.046671906096128105, "sim_render-ego0": 0.00947160310241341, "sim_render-ego1": 0.008437408626429489, "sim_render-ego2": 0.008671174543944357, "sim_render-ego3": 0.008270985926200732, "get_duckie_state": 3.2557433365375793e-06, "in-drivable-lane": 7.0000000000000995, "deviation-heading": 3.355866733742272, "agent_compute-ego0": 0.12998782072048598, "agent_compute-ego1": 0.1126695533088042, "agent_compute-ego2": 0.1099833387684682, "agent_compute-ego3": 0.11375725385960768, "complete-iteration": 1.5709364087614297, "set_robot_commands": 0.006132957286797391, "deviation-center-line": 1.1572362815813495, "driven_lanedir_consec": 8.76615241756709, "sim_compute_sim_state": 0.05448978287833078, "sim_compute_performance-ego0": 0.006595234115063095, "sim_compute_performance-ego1": 0.004493902807366358, "sim_compute_performance-ego2": 0.00445290507635725, "sim_compute_performance-ego3": 0.00446540642157926}, "LFV_multi-norm-techtrack-000-ego1": {"driven_any": 3.634422435690906, "get_ui_image": 0.08082133729863773, "step_physics": 0.8119632530585661, "survival_time": 25.500000000000227, "driven_lanedir": 3.060634333986818, "get_state_dump": 0.02938689504350935, "get_robot_state": 0.046671906096128105, "sim_render-ego0": 0.00947160310241341, "sim_render-ego1": 0.008437408626429489, "sim_render-ego2": 0.008671174543944357, "sim_render-ego3": 0.008270985926200732, "get_duckie_state": 3.2557433365375793e-06, "in-drivable-lane": 1.3000000000000007, "deviation-heading": 9.103409844428269, "agent_compute-ego0": 0.12998782072048598, "agent_compute-ego1": 0.1126695533088042, "agent_compute-ego2": 0.1099833387684682, "agent_compute-ego3": 0.11375725385960768, "complete-iteration": 1.5709364087614297, "set_robot_commands": 0.006132957286797391, "deviation-center-line": 3.5437718854438516, "driven_lanedir_consec": 3.060634333986818, "sim_compute_sim_state": 0.05448978287833078, "sim_compute_performance-ego0": 0.006595234115063095, "sim_compute_performance-ego1": 0.004493902807366358, "sim_compute_performance-ego2": 0.00445290507635725, "sim_compute_performance-ego3": 0.00446540642157926}, "LFV_multi-norm-techtrack-000-ego2": {"driven_any": 4.131995111470532, "get_ui_image": 0.08082133729863773, "step_physics": 0.8119632530585661, "survival_time": 25.500000000000227, "driven_lanedir": 4.082488962295669, "get_state_dump": 0.02938689504350935, "get_robot_state": 0.046671906096128105, "sim_render-ego0": 0.00947160310241341, "sim_render-ego1": 0.008437408626429489, "sim_render-ego2": 0.008671174543944357, "sim_render-ego3": 0.008270985926200732, "get_duckie_state": 3.2557433365375793e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.200506294096474, "agent_compute-ego0": 0.12998782072048598, "agent_compute-ego1": 0.1126695533088042, "agent_compute-ego2": 0.1099833387684682, "agent_compute-ego3": 0.11375725385960768, "complete-iteration": 1.5709364087614297, "set_robot_commands": 0.006132957286797391, "deviation-center-line": 2.793224769868812, "driven_lanedir_consec": 4.082488962295669, "sim_compute_sim_state": 0.05448978287833078, "sim_compute_performance-ego0": 0.006595234115063095, "sim_compute_performance-ego1": 0.004493902807366358, "sim_compute_performance-ego2": 0.00445290507635725, "sim_compute_performance-ego3": 0.00446540642157926}, "LFV_multi-norm-techtrack-000-ego3": {"driven_any": 12.166247135608948, "get_ui_image": 0.08082133729863773, "step_physics": 0.8119632530585661, "survival_time": 25.500000000000227, "driven_lanedir": 11.573623696993607, "get_state_dump": 0.02938689504350935, "get_robot_state": 0.046671906096128105, "sim_render-ego0": 0.00947160310241341, "sim_render-ego1": 0.008437408626429489, "sim_render-ego2": 0.008671174543944357, "sim_render-ego3": 0.008270985926200732, "get_duckie_state": 3.2557433365375793e-06, "in-drivable-lane": 1.050000000000015, "deviation-heading": 3.939493375876067, "agent_compute-ego0": 0.12998782072048598, "agent_compute-ego1": 0.1126695533088042, "agent_compute-ego2": 0.1099833387684682, "agent_compute-ego3": 0.11375725385960768, "complete-iteration": 1.5709364087614297, "set_robot_commands": 0.006132957286797391, "deviation-center-line": 1.548662468903218, "driven_lanedir_consec": 11.573623696993607, "sim_compute_sim_state": 0.05448978287833078, "sim_compute_performance-ego0": 0.006595234115063095, "sim_compute_performance-ego1": 0.004493902807366358, "sim_compute_performance-ego2": 0.00445290507635725, "sim_compute_performance-ego3": 0.00446540642157926}, "LFV_multi-norm-small_loop-000-ego0": {"driven_any": 3.542051695809606, "get_ui_image": 0.057040678047985766, "step_physics": 0.3574633820456748, "survival_time": 7.99999999999998, "driven_lanedir": 3.485589746288337, "get_state_dump": 0.020449155606098057, "get_robot_state": 0.02428164541351129, "sim_render-ego0": 0.009974059110842875, "sim_render-ego1": 0.008409778523889388, "get_duckie_state": 2.7040517108040566e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.1525089054371125, "agent_compute-ego0": 0.14943902536949016, "agent_compute-ego1": 0.12246457982507553, "complete-iteration": 0.7957370503348593, "set_robot_commands": 0.006444662994479541, "deviation-center-line": 0.5244579909543857, "driven_lanedir_consec": 3.485589746288337, "sim_compute_sim_state": 0.024026445720506752, "sim_compute_performance-ego0": 0.005993511365807574, "sim_compute_performance-ego1": 0.0041172074975434295}, "LFV_multi-norm-small_loop-000-ego1": {"driven_any": 3.222519519891979, "get_ui_image": 0.057040678047985766, "step_physics": 0.3574633820456748, "survival_time": 7.99999999999998, "driven_lanedir": 3.1399072970072126, "get_state_dump": 0.020449155606098057, "get_robot_state": 0.02428164541351129, "sim_render-ego0": 0.009974059110842875, "sim_render-ego1": 0.008409778523889388, "get_duckie_state": 2.7040517108040566e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.6314448400562316, "agent_compute-ego0": 0.14943902536949016, "agent_compute-ego1": 0.12246457982507553, "complete-iteration": 0.7957370503348593, "set_robot_commands": 0.006444662994479541, "deviation-center-line": 0.46580390692958307, "driven_lanedir_consec": 3.1399072970072126, "sim_compute_sim_state": 0.024026445720506752, "sim_compute_performance-ego0": 0.005993511365807574, "sim_compute_performance-ego1": 0.0041172074975434295}}
set_robot_commands_max0.0099511378938971
set_robot_commands_mean0.007566749885357808
set_robot_commands_median0.007177197920818072
set_robot_commands_min0.006132957286797391
sim_compute_performance-ego0_max0.007430179024431249
sim_compute_performance-ego0_mean0.006809181156335483
sim_compute_performance-ego0_median0.006809965224776065
sim_compute_performance-ego0_min0.005993511365807574
sim_compute_performance-ego1_max0.006208084120216783
sim_compute_performance-ego1_mean0.004955659512160532
sim_compute_performance-ego1_median0.004584217616207008
sim_compute_performance-ego1_min0.0041172074975434295
sim_compute_sim_state_max0.06619628245709222
sim_compute_sim_state_mean0.049959290946502304
sim_compute_sim_state_median0.05448978287833078
sim_compute_sim_state_min0.024026445720506752
sim_render-ego0_max0.01224734722922425
sim_render-ego0_mean0.010531670602518887
sim_render-ego0_median0.010154867221757012
sim_render-ego0_min0.00947160310241341
sim_render-ego1_max0.011508578427862176
sim_render-ego1_mean0.00951856795113036
sim_render-ego1_median0.009164111512719904
sim_render-ego1_min0.008409778523889388
simulation-passed1
step_physics_max0.9483486447378854
step_physics_mean0.6961835449464351
step_physics_median0.8119632530585661
step_physics_min0.3574633820456748
survival_time_max48.099999999999405
survival_time_mean26.114285714285625
survival_time_min7.99999999999998
No reset possible
6233013610Raphael Jeanmobile-segmentation-pedestrianaido-LF-sim-testing348failedyesreg020:03:27
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6232913610Raphael Jeanmobile-segmentation-pedestrianaido-LF-sim-testing348failedyesreg020:03:38
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6232213640Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LF-sim-testing348successyesreg021:15:34
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
driven_lanedir_consec_median25.42065715360476
survival_time_median59.99999999999873
deviation-center-line_median4.030865820526193
in-drivable-lane_median2.199999999999901


other stats
agent_compute-ego0_max0.2274537245300191
agent_compute-ego0_mean0.2238056463166935
agent_compute-ego0_median0.22445987672432577
agent_compute-ego0_min0.2188491072881033
complete-iteration_max0.6510675483500332
complete-iteration_mean0.5930479593221393
complete-iteration_median0.595431553434075
complete-iteration_min0.530261182070374
deviation-center-line_max4.389772966644217
deviation-center-line_mean3.972758283342981
deviation-center-line_min3.4395285256753194
deviation-heading_max14.762193489667789
deviation-heading_mean11.289516678578218
deviation-heading_median10.52002041827193
deviation-heading_min9.355832388101229
driven_any_max27.544906394931143
driven_any_mean27.254343619776115
driven_any_median27.26781283735796
driven_any_min26.936842409457405
driven_lanedir_consec_max26.234796605727627
driven_lanedir_consec_mean25.3347827470533
driven_lanedir_consec_min24.263020075276057
driven_lanedir_max26.234796605727627
driven_lanedir_mean25.3347827470533
driven_lanedir_median25.42065715360476
driven_lanedir_min24.263020075276057
get_duckie_state_max3.0162630232049464e-06
get_duckie_state_mean2.95864354561608e-06
get_duckie_state_median2.955020615500673e-06
get_duckie_state_min2.9082699282580273e-06
get_robot_state_max0.01260021266889612
get_robot_state_mean0.011812998392897582
get_robot_state_median0.011556497025152329
get_robot_state_min0.01153878685238955
get_state_dump_max0.01875158471926166
get_state_dump_mean0.017680763999786502
get_state_dump_median0.018633766932650273
get_state_dump_min0.014703937414583814
get_ui_image_max0.06339150562969274
get_ui_image_mean0.05470833214593866
get_ui_image_median0.05492527022349844
get_ui_image_min0.04559128250706503
in-drivable-lane_max2.8999999999998813
in-drivable-lane_mean2.2249999999999117
in-drivable-lane_min1.5999999999999632
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 27.169510682266186, "get_ui_image": 0.05243357373316222, "step_physics": 0.22241846171148016, "survival_time": 59.99999999999873, "driven_lanedir": 25.79274927781033, "get_state_dump": 0.01856255987899488, "get_robot_state": 0.01153878685238955, "sim_render-ego0": 0.010632417084870983, "get_duckie_state": 2.9082699282580273e-06, "in-drivable-lane": 1.7499999999999138, "deviation-heading": 9.355832388101229, "agent_compute-ego0": 0.2274537245300191, "complete-iteration": 0.5812515156354435, "set_robot_commands": 0.007976325525034477, "deviation-center-line": 3.4395285256753194, "driven_lanedir_consec": 25.79274927781033, "sim_compute_sim_state": 0.023387640937976698, "sim_compute_performance-ego0": 0.006654767370740142}, "LF-norm-zigzag-000-ego0": {"driven_any": 26.936842409457405, "get_ui_image": 0.06339150562969274, "step_physics": 0.27728976020209495, "survival_time": 59.99999999999873, "driven_lanedir": 24.263020075276057, "get_state_dump": 0.014703937414583814, "get_robot_state": 0.011540661445763784, "sim_render-ego0": 0.010867418992727822, "get_duckie_state": 3.0162630232049464e-06, "in-drivable-lane": 2.6499999999998884, "deviation-heading": 14.762193489667789, "agent_compute-ego0": 0.22735392580818475, "complete-iteration": 0.6510675483500332, "set_robot_commands": 0.008303713937484651, "deviation-center-line": 4.389772966644217, "driven_lanedir_consec": 24.263020075276057, "sim_compute_sim_state": 0.03094216250658631, "sim_compute_performance-ego0": 0.006477039719898436}, "LF-norm-techtrack-000-ego0": {"driven_any": 27.36611499244974, "get_ui_image": 0.057416966713834665, "step_physics": 0.2471878457128952, "survival_time": 59.99999999999873, "driven_lanedir": 25.04856502939919, "get_state_dump": 0.018704973986305665, "get_robot_state": 0.011572332604540872, "sim_render-ego0": 0.010049033026016323, "get_duckie_state": 2.9116447124751183e-06, "in-drivable-lane": 2.8999999999998813, "deviation-heading": 11.618042535466229, "agent_compute-ego0": 0.2188491072881033, "complete-iteration": 0.6096115912327064, "set_robot_commands": 0.008839709474879637, "deviation-center-line": 3.743252930625733, "driven_lanedir_consec": 25.04856502939919, "sim_compute_sim_state": 0.030057407834944774, "sim_compute_performance-ego0": 0.00673728858700005}, "LF-norm-small_loop-000-ego0": {"driven_any": 27.544906394931143, "get_ui_image": 0.04559128250706503, "step_physics": 0.1875074691915393, "survival_time": 59.99999999999873, "driven_lanedir": 26.234796605727627, "get_state_dump": 0.01875158471926166, "get_robot_state": 0.01260021266889612, "sim_render-ego0": 0.011050880203437646, "get_duckie_state": 2.9983965185262284e-06, "in-drivable-lane": 1.5999999999999632, "deviation-heading": 9.42199830107763, "agent_compute-ego0": 0.22156582764046676, "complete-iteration": 0.530261182070374, "set_robot_commands": 0.00875797041449122, "deviation-center-line": 4.318478710426653, "driven_lanedir_consec": 26.234796605727627, "sim_compute_sim_state": 0.01726508001602262, "sim_compute_performance-ego0": 0.006970647769010037}}
set_robot_commands_max0.008839709474879637
set_robot_commands_mean0.008469429837972497
set_robot_commands_median0.008530842175987936
set_robot_commands_min0.007976325525034477
sim_compute_performance-ego0_max0.006970647769010037
sim_compute_performance-ego0_mean0.006709935861662166
sim_compute_performance-ego0_median0.006696027978870096
sim_compute_performance-ego0_min0.006477039719898436
sim_compute_sim_state_max0.03094216250658631
sim_compute_sim_state_mean0.0254130728238826
sim_compute_sim_state_median0.026722524386460736
sim_compute_sim_state_min0.01726508001602262
sim_render-ego0_max0.011050880203437646
sim_render-ego0_mean0.010649937326763196
sim_render-ego0_median0.010749918038799404
sim_render-ego0_min0.010049033026016323
simulation-passed1
step_physics_max0.27728976020209495
step_physics_mean0.2336008842045024
step_physics_median0.23480315371218768
step_physics_min0.1875074691915393
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
No reset possible
6232113647Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LFP-sim-validation350successyesreg020:09:02
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median4.874999999999991
in-drivable-lane_median0.0
driven_lanedir_consec_median1.66437979919727
deviation-center-line_median0.2931924266881385


other stats
agent_compute-ego0_max0.0488516631191724
agent_compute-ego0_mean0.041232997432429225
agent_compute-ego0_median0.039670541497743184
agent_compute-ego0_min0.03673924361505816
complete-iteration_max0.5544315592883384
complete-iteration_mean0.4839245576156004
complete-iteration_median0.5060362067650741
complete-iteration_min0.369194257643915
deviation-center-line_max0.6893377922523443
deviation-center-line_mean0.3653825516527035
deviation-center-line_min0.1858075609821929
deviation-heading_max2.72597870522281
deviation-heading_mean1.2702278649970735
deviation-heading_median0.97078389913569
deviation-heading_min0.4133649564941046
driven_any_max4.216492410954944
driven_any_mean2.0491563007627427
driven_any_median1.7162703843341198
driven_any_min0.5475920234277866
driven_lanedir_consec_max3.5944696527910818
driven_lanedir_consec_mean1.865023113074084
driven_lanedir_consec_min0.5368632011107151
driven_lanedir_max3.5944696527910818
driven_lanedir_mean1.865023113074084
driven_lanedir_median1.66437979919727
driven_lanedir_min0.5368632011107151
get_duckie_state_max0.08594009350163276
get_duckie_state_mean0.06287323367036214
get_duckie_state_median0.0765039940539016
get_duckie_state_min0.012544853072012623
get_robot_state_max0.014960919340995893
get_robot_state_mean0.012081888710926894
get_robot_state_median0.01151749743090856
get_robot_state_min0.010331640640894571
get_state_dump_max0.041794117182901463
get_state_dump_mean0.027089689641729944
get_state_dump_median0.024563873352858943
get_state_dump_min0.017436894678300428
get_ui_image_max0.07032173627043424
get_ui_image_mean0.05850019206511708
get_ui_image_median0.05856698146308532
get_ui_image_min0.04654506906386344
in-drivable-lane_max0.9500000000000064
in-drivable-lane_mean0.2375000000000016
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 4.216492410954944, "get_ui_image": 0.05495449187050403, "step_physics": 0.2525896589520951, "survival_time": 10.600000000000016, "driven_lanedir": 3.5944696527910818, "get_state_dump": 0.025324954673158172, "get_robot_state": 0.011068870204155434, "sim_render-ego0": 0.010351741817635547, "get_duckie_state": 0.08594009350163276, "in-drivable-lane": 0.9500000000000064, "deviation-heading": 2.72597870522281, "agent_compute-ego0": 0.03860599222317548, "complete-iteration": 0.5133142415346674, "set_robot_commands": 0.007559954280584631, "deviation-center-line": 0.6893377922523443, "driven_lanedir_consec": 3.5944696527910818, "sim_compute_sim_state": 0.02066239952481409, "sim_compute_performance-ego0": 0.006044525495717224}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5475920234277866, "get_ui_image": 0.0621794710556666, "step_physics": 0.24425501128037772, "survival_time": 2.3499999999999996, "driven_lanedir": 0.5368632011107151, "get_state_dump": 0.02380279203255971, "get_robot_state": 0.010331640640894571, "sim_render-ego0": 0.008790045976638794, "get_duckie_state": 0.07526664932568868, "in-drivable-lane": 0.0, "deviation-heading": 0.4133649564941046, "agent_compute-ego0": 0.04073509077231089, "complete-iteration": 0.4987581719954808, "set_robot_commands": 0.005192289749781291, "deviation-center-line": 0.2479913532783351, "driven_lanedir_consec": 0.5368632011107151, "sim_compute_sim_state": 0.022442201773325603, "sim_compute_performance-ego0": 0.005547339717547099}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.171515563374533, "get_ui_image": 0.07032173627043424, "step_physics": 0.24215390257639427, "survival_time": 3.599999999999995, "driven_lanedir": 1.1357577795739768, "get_state_dump": 0.041794117182901463, "get_robot_state": 0.014960919340995893, "sim_render-ego0": 0.007884058233809797, "get_duckie_state": 0.07774133878211452, "in-drivable-lane": 0.0, "deviation-heading": 0.8182272241761924, "agent_compute-ego0": 0.0488516631191724, "complete-iteration": 0.5544315592883384, "set_robot_commands": 0.009155838456872392, "deviation-center-line": 0.1858075609821929, "driven_lanedir_consec": 1.1357577795739768, "sim_compute_sim_state": 0.03498439592857883, "sim_compute_performance-ego0": 0.006365407003115301}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.2610252052937065, "get_ui_image": 0.04654506906386344, "step_physics": 0.20015890559842509, "survival_time": 6.149999999999986, "driven_lanedir": 2.193001818820563, "get_state_dump": 0.017436894678300428, "get_robot_state": 0.011966124657661684, "sim_render-ego0": 0.013740462641562184, "get_duckie_state": 0.012544853072012623, "in-drivable-lane": 0.0, "deviation-heading": 1.1233405740951876, "agent_compute-ego0": 0.03673924361505816, "complete-iteration": 0.369194257643915, "set_robot_commands": 0.007157268062714607, "deviation-center-line": 0.3383935000979419, "driven_lanedir_consec": 2.193001818820563, "sim_compute_sim_state": 0.016260902727803877, "sim_compute_performance-ego0": 0.00643295818759549}}
set_robot_commands_max0.009155838456872392
set_robot_commands_mean0.00726633763748823
set_robot_commands_median0.007358611171649619
set_robot_commands_min0.005192289749781291
sim_compute_performance-ego0_max0.00643295818759549
sim_compute_performance-ego0_mean0.006097557600993779
sim_compute_performance-ego0_median0.006204966249416263
sim_compute_performance-ego0_min0.005547339717547099
sim_compute_sim_state_max0.03498439592857883
sim_compute_sim_state_mean0.0235874749886306
sim_compute_sim_state_median0.021552300649069843
sim_compute_sim_state_min0.016260902727803877
sim_render-ego0_max0.013740462641562184
sim_render-ego0_mean0.01019157716741158
sim_render-ego0_median0.00957089389713717
sim_render-ego0_min0.007884058233809797
simulation-passed1
step_physics_max0.2525896589520951
step_physics_mean0.23478936960182303
step_physics_median0.243204456928386
step_physics_min0.20015890559842509
survival_time_max10.600000000000016
survival_time_mean5.674999999999999
survival_time_min2.3499999999999996
No reset possible
6231913617Raphael Jeanmobile-segmentation-pedestrianaido-LFP-sim-validation350failedyesreg020:04:01
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6230213691Caleb BG 🇺🇸baseline-duckietownaido-LF-sim-validation347successyesreg020:47:37
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
driven_lanedir_consec_median0.0
survival_time_median59.99999999999873
deviation-center-line_median1.2422730096440104
in-drivable-lane_median0.0


other stats
agent_compute-ego0_max0.04157347464740128
agent_compute-ego0_mean0.0403371485544184
agent_compute-ego0_median0.04089011746977489
agent_compute-ego0_min0.03799488463072256
complete-iteration_max0.2997927548585585
complete-iteration_mean0.2852930716432004
complete-iteration_median0.28552629250868666
complete-iteration_min0.27032694669686985
deviation-center-line_max4.053503393024394
deviation-center-line_mean1.731069028434027
deviation-center-line_min0.386226701423694
deviation-heading_max27.859809596736422
deviation-heading_mean14.925250437835436
deviation-heading_median14.309269950178932
deviation-heading_min3.22265225424745
driven_any_max2.6645352591003757e-13
driven_any_mean1.9984014443252818e-13
driven_any_median2.6645352591003757e-13
driven_any_min0.0
driven_lanedir_consec_max0.000286102294921875
driven_lanedir_consec_mean7.152557373046875e-05
driven_lanedir_consec_min0.0
driven_lanedir_max0.000286102294921875
driven_lanedir_mean7.152557373046875e-05
driven_lanedir_median0.0
driven_lanedir_min0.0
get_duckie_state_max3.1736867810963195e-06
get_duckie_state_mean3.0815750236415944e-06
get_duckie_state_median3.157507668526147e-06
get_duckie_state_min2.837597976417764e-06
get_robot_state_max0.014137498345799889
get_robot_state_mean0.01334481106312646
get_robot_state_median0.013314274328138111
get_robot_state_min0.012613197250429736
get_state_dump_max0.02289710592766983
get_state_dump_mean0.01971719564744376
get_state_dump_median0.01923110095984136
get_state_dump_min0.017509474742422492
get_ui_image_max0.06284125837854898
get_ui_image_mean0.05557011098885515
get_ui_image_median0.05493704663228234
get_ui_image_min0.049565092312306984
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 2.6645352591003757e-13, "get_ui_image": 0.04980510120884167, "step_physics": 0.10121412201785326, "survival_time": 59.99999999999873, "driven_lanedir": 0.0, "get_state_dump": 0.01810237132540154, "get_robot_state": 0.013223467619591808, "sim_render-ego0": 0.0110370247290593, "get_duckie_state": 3.1736867810963195e-06, "in-drivable-lane": 0.0, "deviation-heading": 22.66279310353771, "agent_compute-ego0": 0.03799488463072256, "complete-iteration": 0.27032694669686985, "set_robot_commands": 0.008345062190745892, "deviation-center-line": 4.053503393024394, "driven_lanedir_consec": 0.0, "sim_compute_sim_state": 0.023663578581353407, "sim_compute_performance-ego0": 0.0067408059062211345}, "LF-norm-zigzag-000-ego0": {"driven_any": 0.0, "get_ui_image": 0.06284125837854898, "step_physics": 0.10971719617152788, "survival_time": 59.99999999999873, "driven_lanedir": 0.000286102294921875, "get_state_dump": 0.017509474742422492, "get_robot_state": 0.012613197250429736, "sim_render-ego0": 0.011289922919102652, "get_duckie_state": 3.1675327628180943e-06, "in-drivable-lane": 0.0, "deviation-heading": 27.859809596736422, "agent_compute-ego0": 0.041223901396091535, "complete-iteration": 0.2997927548585585, "set_robot_commands": 0.008364552760699905, "deviation-center-line": 1.0457540566888746, "driven_lanedir_consec": 0.000286102294921875, "sim_compute_sim_state": 0.02927589436355578, "sim_compute_performance-ego0": 0.006760102525341025}, "LF-norm-techtrack-000-ego0": {"driven_any": 2.6645352591003757e-13, "get_ui_image": 0.06006899205572301, "step_physics": 0.11124242076667322, "survival_time": 59.99999999999873, "driven_lanedir": 0.0, "get_state_dump": 0.020359830594281175, "get_robot_state": 0.013405081036684415, "sim_render-ego0": 0.010465397227316674, "get_duckie_state": 2.837597976417764e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.22265225424745, "agent_compute-ego0": 0.04157347464740128, "complete-iteration": 0.297173440903053, "set_robot_commands": 0.007973548474657248, "deviation-center-line": 1.4387919625991463, "driven_lanedir_consec": 0.0, "sim_compute_sim_state": 0.02539472476727361, "sim_compute_performance-ego0": 0.006502065531518636}, "LF-norm-small_loop-000-ego0": {"driven_any": 2.6645352591003757e-13, "get_ui_image": 0.049565092312306984, "step_physics": 0.10273182818931306, "survival_time": 59.99999999999873, "driven_lanedir": 0.0, "get_state_dump": 0.02289710592766983, "get_robot_state": 0.014137498345799889, "sim_render-ego0": 0.011053168704170273, "get_duckie_state": 3.1474825742341996e-06, "in-drivable-lane": 0.0, "deviation-heading": 5.955746796820156, "agent_compute-ego0": 0.04055633354345825, "complete-iteration": 0.2738791441143204, "set_robot_commands": 0.008803255651316774, "deviation-center-line": 0.386226701423694, "driven_lanedir_consec": 0.0, "sim_compute_sim_state": 0.017139079271010018, "sim_compute_performance-ego0": 0.006802670465321664}}
set_robot_commands_max0.008803255651316774
set_robot_commands_mean0.008371604769354954
set_robot_commands_median0.008354807475722897
set_robot_commands_min0.007973548474657248
sim_compute_performance-ego0_max0.006802670465321664
sim_compute_performance-ego0_mean0.006701411107100615
sim_compute_performance-ego0_median0.00675045421578108
sim_compute_performance-ego0_min0.006502065531518636
sim_compute_sim_state_max0.02927589436355578
sim_compute_sim_state_mean0.0238683192457982
sim_compute_sim_state_median0.024529151674313507
sim_compute_sim_state_min0.017139079271010018
sim_render-ego0_max0.011289922919102652
sim_render-ego0_mean0.010961378394912224
sim_render-ego0_median0.011045096716614786
sim_render-ego0_min0.010465397227316674
simulation-passed1
step_physics_max0.11124242076667322
step_physics_mean0.10622639178634186
step_physics_median0.10622451218042048
step_physics_min0.10121412201785326
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
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driven_lanedir_consec_median0.399735831262527
survival_time_median5.699999999999988
deviation-center-line_median0.1326287550235055
in-drivable-lane_median4.04999999999999


other stats
agent_compute-ego0_max0.4070396488363093
agent_compute-ego0_mean0.12311127220208828
agent_compute-ego0_median0.029169455587447105
agent_compute-ego0_min0.027066528797149655
complete-iteration_max0.8291410272771662
complete-iteration_mean0.4774626049816192
complete-iteration_median0.3884982683983717
complete-iteration_min0.30371285585256724
deviation-center-line_max0.1607014721617163
deviation-center-line_mean0.11802894242427787
deviation-center-line_min0.04615678748838428
deviation-heading_max1.570262276455506
deviation-heading_mean0.9596984587337092
deviation-heading_median0.9758940962111282
deviation-heading_min0.31674336605707476
driven_any_max3.851436114382767
driven_any_mean2.3012055677881875
driven_any_median2.1300739843964096
driven_any_min1.0932381879771642
driven_lanedir_consec_max0.48340597962598864
driven_lanedir_consec_mean0.35618583073384297
driven_lanedir_consec_min0.14186568078432926
driven_lanedir_max0.48340597962598864
driven_lanedir_mean0.35618583073384297
driven_lanedir_median0.399735831262527
driven_lanedir_min0.14186568078432926
get_duckie_state_max4.078041423450817e-06
get_duckie_state_mean4.011392593383789e-06
get_duckie_state_median4.041194915771485e-06
get_duckie_state_min3.885139118541371e-06
get_robot_state_max0.014717634157700972
get_robot_state_mean0.011676803397965596
get_robot_state_median0.011066542221949652
get_robot_state_min0.009856494990262116
get_state_dump_max0.021836330673911355
get_state_dump_mean0.017460858696824188
get_state_dump_median0.01743816274863023
get_state_dump_min0.013130778616124932
get_ui_image_max0.06395623467185281
get_ui_image_mean0.05567961981038114
get_ui_image_median0.05625819634307515
get_ui_image_min0.04624585188352145
in-drivable-lane_max10.10000000000002
in-drivable-lane_mean5.049999999999999
in-drivable-lane_min1.9999999999999971
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 1.8661719467483455, "get_ui_image": 0.0511736249923706, "step_physics": 0.16717329502105713, "survival_time": 4.94999999999999, "driven_lanedir": 0.3990879078270344, "get_state_dump": 0.01866492033004761, "get_robot_state": 0.011564040184020996, "sim_render-ego0": 0.012756738662719726, "get_duckie_state": 4.00543212890625e-06, "in-drivable-lane": 3.3999999999999932, "deviation-heading": 0.932010914136802, "agent_compute-ego0": 0.027066528797149655, "complete-iteration": 0.32418944358825685, "set_robot_commands": 0.006422379016876221, "deviation-center-line": 0.1607014721617163, "driven_lanedir_consec": 0.3990879078270344, "sim_compute_sim_state": 0.02242551565170288, "sim_compute_performance-ego0": 0.006738615036010742}, "LF-norm-zigzag-000-ego0": {"driven_any": 1.0932381879771642, "get_ui_image": 0.061342767693779686, "step_physics": 0.29197645458308136, "survival_time": 4.3499999999999925, "driven_lanedir": 0.48340597962598864, "get_state_dump": 0.013130778616124932, "get_robot_state": 0.009856494990262116, "sim_render-ego0": 0.008198123086582531, "get_duckie_state": 3.885139118541371e-06, "in-drivable-lane": 1.9999999999999971, "deviation-heading": 1.570262276455506, "agent_compute-ego0": 0.029817437583749943, "complete-iteration": 0.4528070932084864, "set_robot_commands": 0.00746132027019154, "deviation-center-line": 0.14628707747786857, "driven_lanedir_consec": 0.48340597962598864, "sim_compute_sim_state": 0.02471564303744923, "sim_compute_performance-ego0": 0.006101651625199752}, "LF-norm-techtrack-000-ego0": {"driven_any": 3.851436114382767, "get_ui_image": 0.06395623467185281, "step_physics": 0.2683737592263655, "survival_time": 10.95000000000002, "driven_lanedir": 0.14186568078432926, "get_state_dump": 0.021836330673911355, "get_robot_state": 0.014717634157700972, "sim_render-ego0": 0.012746437029405071, "get_duckie_state": 4.078041423450817e-06, "in-drivable-lane": 10.10000000000002, "deviation-heading": 0.31674336605707476, "agent_compute-ego0": 0.4070396488363093, "complete-iteration": 0.8291410272771662, "set_robot_commands": 0.007018132643266158, "deviation-center-line": 0.04615678748838428, "driven_lanedir_consec": 0.14186568078432926, "sim_compute_sim_state": 0.026689469814300537, "sim_compute_performance-ego0": 0.0065488100051879885}, "LF-norm-small_loop-000-ego0": {"driven_any": 2.3939760220444732, "get_ui_image": 0.04624585188352145, "step_physics": 0.16439942580003006, "survival_time": 6.449999999999985, "driven_lanedir": 0.4003837546980196, "get_state_dump": 0.016211405167212853, "get_robot_state": 0.010569044259878306, "sim_render-ego0": 0.009111925271841195, "get_duckie_state": 4.076957702636719e-06, "in-drivable-lane": 4.699999999999988, "deviation-heading": 1.0197772782854544, "agent_compute-ego0": 0.028521473591144268, "complete-iteration": 0.30371285585256724, "set_robot_commands": 0.007876416353078989, "deviation-center-line": 0.1189704325691424, "driven_lanedir_consec": 0.4003837546980196, "sim_compute_sim_state": 0.014449530381422776, "sim_compute_performance-ego0": 0.006121327326847957}}
set_robot_commands_max0.007876416353078989
set_robot_commands_mean0.007194562070853227
set_robot_commands_median0.007239726456728849
set_robot_commands_min0.006422379016876221
sim_compute_performance-ego0_max0.006738615036010742
sim_compute_performance-ego0_mean0.00637760099831161
sim_compute_performance-ego0_median0.006335068666017973
sim_compute_performance-ego0_min0.006101651625199752
sim_compute_sim_state_max0.026689469814300537
sim_compute_sim_state_mean0.022070039721218855
sim_compute_sim_state_median0.023570579344576056
sim_compute_sim_state_min0.014449530381422776
sim_render-ego0_max0.012756738662719726
sim_render-ego0_mean0.010703306012637132
sim_render-ego0_median0.010929181150623137
sim_render-ego0_min0.008198123086582531
simulation-passed1
step_physics_max0.29197645458308136
step_physics_mean0.22298073365763357
step_physics_median0.21777352712371137
step_physics_min0.16439942580003006
survival_time_max10.95000000000002
survival_time_mean6.674999999999997
survival_time_min4.3499999999999925
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driven_lanedir_consec_median0.362616513347588
survival_time_median4.974999999999991
deviation-center-line_median0.12969313506655755
in-drivable-lane_median3.299999999999993


other stats
agent_compute-ego0_max0.8574547408378288
agent_compute-ego0_mean0.23754021153624752
agent_compute-ego0_median0.03139166737229415
agent_compute-ego0_min0.029922770562573017
complete-iteration_max1.191720283194764
complete-iteration_mean0.5724377809270342
complete-iteration_median0.3905587814842495
complete-iteration_min0.3169132775448738
deviation-center-line_max0.15327191211280158
deviation-center-line_mean0.11446879369200195
deviation-center-line_min0.04521699252209115
deviation-heading_max1.4057096617083424
deviation-heading_mean0.9034562215616172
deviation-heading_median0.9527757282234824
deviation-heading_min0.302563768091162
driven_any_max2.5127280143679083
driven_any_mean1.696515059421674
driven_any_median1.6050395998681983
driven_any_min1.0632530235823918
driven_lanedir_consec_max0.42756338037792774
driven_lanedir_consec_mean0.32519473648403996
driven_lanedir_consec_min0.147982538863056
driven_lanedir_max0.42756338037792774
driven_lanedir_mean0.32519473648403996
driven_lanedir_median0.362616513347588
driven_lanedir_min0.147982538863056
get_duckie_state_max4.010779835353388e-06
get_duckie_state_mean3.813152011769342e-06
get_duckie_state_median3.837673774499619e-06
get_duckie_state_min3.566480662724743e-06
get_robot_state_max0.01136388511301201
get_robot_state_mean0.010185689018083232
get_robot_state_median0.009987478168944693
get_robot_state_min0.009403914621431534
get_state_dump_max0.02421845933970283
get_state_dump_mean0.020225434254840195
get_state_dump_median0.020721163776243723
get_state_dump_min0.01524095012717051
get_ui_image_max0.06609065510402216
get_ui_image_mean0.05582826137503257
get_ui_image_median0.052474751228002846
get_ui_image_min0.05227288794010244
in-drivable-lane_max4.949999999999987
in-drivable-lane_mean3.574999999999992
in-drivable-lane_min2.749999999999995
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 1.7212581220165153, "get_ui_image": 0.05227288794010244, "step_physics": 0.16181825069670985, "survival_time": 4.6499999999999915, "driven_lanedir": 0.38662562014949065, "get_state_dump": 0.018731796995122382, "get_robot_state": 0.009417779902194409, "sim_render-ego0": 0.010752518126305111, "get_duckie_state": 3.80455179417387e-06, "in-drivable-lane": 3.149999999999994, "deviation-heading": 0.8885233490686405, "agent_compute-ego0": 0.030854925196221537, "complete-iteration": 0.3169132775448738, "set_robot_commands": 0.006615585469185037, "deviation-center-line": 0.15327191211280158, "driven_lanedir_consec": 0.38662562014949065, "sim_compute_sim_state": 0.02076632672167839, "sim_compute_performance-ego0": 0.005496788532175916}, "LF-norm-zigzag-000-ego0": {"driven_any": 1.4888210777198814, "get_ui_image": 0.06609065510402216, "step_physics": 0.276834594869168, "survival_time": 5.299999999999989, "driven_lanedir": 0.33860740654568544, "get_state_dump": 0.02271053055736506, "get_robot_state": 0.01136388511301201, "sim_render-ego0": 0.01031698467575501, "get_duckie_state": 4.010779835353388e-06, "in-drivable-lane": 3.449999999999992, "deviation-heading": 1.4057096617083424, "agent_compute-ego0": 0.029922770562573017, "complete-iteration": 0.456195811244929, "set_robot_commands": 0.0067776630972033345, "deviation-center-line": 0.1375699127153115, "driven_lanedir_consec": 0.33860740654568544, "sim_compute_sim_state": 0.024953216035789417, "sim_compute_performance-ego0": 0.007030560591510523}, "LF-norm-techtrack-000-ego0": {"driven_any": 1.0632530235823918, "get_ui_image": 0.05260851285228991, "step_physics": 0.21579111765508785, "survival_time": 3.599999999999995, "driven_lanedir": 0.147982538863056, "get_state_dump": 0.01524095012717051, "get_robot_state": 0.009403914621431534, "sim_render-ego0": 0.009332898544938595, "get_duckie_state": 3.566480662724743e-06, "in-drivable-lane": 2.749999999999995, "deviation-heading": 0.302563768091162, "agent_compute-ego0": 0.8574547408378288, "complete-iteration": 1.191720283194764, "set_robot_commands": 0.005638700641997873, "deviation-center-line": 0.04521699252209115, "driven_lanedir_consec": 0.147982538863056, "sim_compute_sim_state": 0.020575977351567517, "sim_compute_performance-ego0": 0.005476624998327804}, "LF-norm-small_loop-000-ego0": {"driven_any": 2.5127280143679083, "get_ui_image": 0.05234098960371578, "step_physics": 0.16576771350467906, "survival_time": 6.749999999999984, "driven_lanedir": 0.42756338037792774, "get_state_dump": 0.02421845933970283, "get_robot_state": 0.010557176435694976, "sim_render-ego0": 0.010224989231894998, "get_duckie_state": 3.8707957548253674e-06, "in-drivable-lane": 4.949999999999987, "deviation-heading": 1.0170281073783245, "agent_compute-ego0": 0.031928409548366773, "complete-iteration": 0.32492175172356996, "set_robot_commands": 0.006413673653322107, "deviation-center-line": 0.1218163574178036, "driven_lanedir_consec": 0.42756338037792774, "sim_compute_sim_state": 0.01785963773727417, "sim_compute_performance-ego0": 0.0054282090243171245}}
set_robot_commands_max0.0067776630972033345
set_robot_commands_mean0.006361405715427089
set_robot_commands_median0.006514629561253572
set_robot_commands_min0.005638700641997873
sim_compute_performance-ego0_max0.007030560591510523
sim_compute_performance-ego0_mean0.005858045786582842
sim_compute_performance-ego0_median0.00548670676525186
sim_compute_performance-ego0_min0.0054282090243171245
sim_compute_sim_state_max0.024953216035789417
sim_compute_sim_state_mean0.021038789461577372
sim_compute_sim_state_median0.020671152036622953
sim_compute_sim_state_min0.01785963773727417
sim_render-ego0_max0.010752518126305111
sim_render-ego0_mean0.01015684764472343
sim_render-ego0_median0.010270986953825004
sim_render-ego0_min0.009332898544938595
simulation-passed1
step_physics_max0.276834594869168
step_physics_mean0.20505291918141116
step_physics_median0.19077941557988343
step_physics_min0.16181825069670985
survival_time_max6.749999999999984
survival_time_mean5.07499999999999
survival_time_min3.599999999999995
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survival_time_median8.774999999999991
in-drivable-lane_median0.1999999999999993
driven_lanedir_consec_median2.825400245305862
deviation-center-line_median0.5774585366662469


other stats
agent_compute-ego0_max0.10348106593620486
agent_compute-ego0_mean0.0987295627018609
agent_compute-ego0_median0.09882939590003512
agent_compute-ego0_min0.09377839307116854
complete-iteration_max0.5795385809058687
complete-iteration_mean0.5043370811872153
complete-iteration_median0.5225426581975179
complete-iteration_min0.3927244274479568
deviation-center-line_max0.8348774251354573
deviation-center-line_mean0.5330636690236606
deviation-center-line_min0.14246017762669144
deviation-heading_max3.135654468709013
deviation-heading_mean1.9348831674892233
deviation-heading_median2.150519199210233
deviation-heading_min0.30283980282741496
driven_any_max4.340091312692358
driven_any_mean2.800310061195733
driven_any_median3.1703641673475187
driven_any_min0.5204205973955357
driven_lanedir_consec_max4.003504886630219
driven_lanedir_consec_mean2.5411955013358556
driven_lanedir_consec_min0.5104766281014785
driven_lanedir_max4.003504886630219
driven_lanedir_mean2.5411955013358556
driven_lanedir_median2.825400245305862
driven_lanedir_min0.5104766281014785
get_duckie_state_max0.07544500653336687
get_duckie_state_mean0.05759446976864549
get_duckie_state_median0.07022456152718845
get_duckie_state_min0.014483749486838176
get_robot_state_max0.0146431515856487
get_robot_state_mean0.013359385971032372
get_robot_state_median0.013502494354916223
get_robot_state_min0.011789403588648335
get_state_dump_max0.039120993963102015
get_state_dump_mean0.02708196556384765
get_state_dump_median0.02599066244372411
get_state_dump_min0.01722554340484036
get_ui_image_max0.0629778221783885
get_ui_image_mean0.05643318920008112
get_ui_image_median0.05886552190234734
get_ui_image_min0.045023890817241306
in-drivable-lane_max1.3000000000000025
in-drivable-lane_mean0.42500000000000027
in-drivable-lane_min0.0
per-episodes
details{"LFP-full-loop-000-ego0": {"driven_any": 0.5204205973955357, "get_ui_image": 0.0570657136963635, "step_physics": 0.13611192819548815, "survival_time": 2.000000000000001, "driven_lanedir": 0.5104766281014785, "get_state_dump": 0.039120993963102015, "get_robot_state": 0.0146431515856487, "sim_render-ego0": 0.00940521751962057, "get_duckie_state": 0.07544500653336687, "in-drivable-lane": 0.0, "deviation-heading": 0.30283980282741496, "agent_compute-ego0": 0.10348106593620486, "complete-iteration": 0.47540888553712424, "set_robot_commands": 0.007976212152620642, "deviation-center-line": 0.14246017762669144, "driven_lanedir_consec": 0.5104766281014785, "sim_compute_sim_state": 0.022944962106099944, "sim_compute_performance-ego0": 0.008977256170133265}, "LFP-full-zigzag-000-ego0": {"driven_any": 4.340091312692358, "get_ui_image": 0.0629778221783885, "step_physics": 0.26277361163105145, "survival_time": 12.500000000000044, "driven_lanedir": 4.003504886630219, "get_state_dump": 0.02269103802532789, "get_robot_state": 0.011789403588648335, "sim_render-ego0": 0.01116732297190632, "get_duckie_state": 0.06631752599283043, "in-drivable-lane": 0.3999999999999986, "deviation-heading": 3.135654468709013, "agent_compute-ego0": 0.0959112558706823, "complete-iteration": 0.5795385809058687, "set_robot_commands": 0.008029292779139788, "deviation-center-line": 0.8348774251354573, "driven_lanedir_consec": 4.003504886630219, "sim_compute_sim_state": 0.03134538547926215, "sim_compute_performance-ego0": 0.006298072784545412}, "LFP-full-techtrack-000-ego0": {"driven_any": 3.5490382518365102, "get_ui_image": 0.060665330108331174, "step_physics": 0.2321964173900838, "survival_time": 9.750000000000004, "driven_lanedir": 2.9522887890193164, "get_state_dump": 0.029290286862120336, "get_robot_state": 0.013143479824066162, "sim_render-ego0": 0.010413644265155404, "get_duckie_state": 0.07413159706154648, "in-drivable-lane": 1.3000000000000025, "deviation-heading": 2.503361387250735, "agent_compute-ego0": 0.10174753592938791, "complete-iteration": 0.5696764308579114, "set_robot_commands": 0.007436805841874103, "deviation-center-line": 0.5039451131855264, "driven_lanedir_consec": 2.9522887890193164, "sim_compute_sim_state": 0.032723426818847656, "sim_compute_performance-ego0": 0.00768236238129285}, "LFP-full-small_loop-000-ego0": {"driven_any": 2.791690082858527, "get_ui_image": 0.045023890817241306, "step_physics": 0.16623987058165726, "survival_time": 7.79999999999998, "driven_lanedir": 2.698511701592407, "get_state_dump": 0.01722554340484036, "get_robot_state": 0.01386150888576629, "sim_render-ego0": 0.010792167323410132, "get_duckie_state": 0.014483749486838176, "in-drivable-lane": 0.0, "deviation-heading": 1.79767701116973, "agent_compute-ego0": 0.09377839307116854, "complete-iteration": 0.3927244274479568, "set_robot_commands": 0.008440363938641397, "deviation-center-line": 0.6509719601469675, "driven_lanedir_consec": 2.698511701592407, "sim_compute_sim_state": 0.016323335611136854, "sim_compute_performance-ego0": 0.00632884547968579}}
set_robot_commands_max0.008440363938641397
set_robot_commands_mean0.007970668678068983
set_robot_commands_median0.008002752465880215
set_robot_commands_min0.007436805841874103
sim_compute_performance-ego0_max0.008977256170133265
sim_compute_performance-ego0_mean0.0073216342039143295
sim_compute_performance-ego0_median0.00700560393048932
sim_compute_performance-ego0_min0.006298072784545412
sim_compute_sim_state_max0.032723426818847656
sim_compute_sim_state_mean0.025834277503836652
sim_compute_sim_state_median0.027145173792681047
sim_compute_sim_state_min0.016323335611136854
sim_render-ego0_max0.01116732297190632
sim_render-ego0_mean0.010444588020023106
sim_render-ego0_median0.010602905794282767
sim_render-ego0_min0.00940521751962057
simulation-passed1
step_physics_max0.26277361163105145
step_physics_mean0.19933045694957016
step_physics_median0.1992181439858705
step_physics_min0.13611192819548815
survival_time_max12.500000000000044
survival_time_mean8.012500000000006
survival_time_min2.000000000000001
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survival_time_median4.024999999999993
in-drivable-lane_median2.1749999999999927
driven_lanedir_consec_median0.7879375916748629
deviation-center-line_median0.17229705763589412


other stats
agent_compute-ego0_max0.10827696323394775
agent_compute-ego0_mean0.09907582793792669
agent_compute-ego0_median0.09794876236626908
agent_compute-ego0_min0.09212882378522089
complete-iteration_max0.4731710507319524
complete-iteration_mean0.4254564987668311
complete-iteration_median0.4332775186789434
complete-iteration_min0.3620999069774852
deviation-center-line_max2.241378044644111
deviation-center-line_mean0.6770129712603397
deviation-center-line_min0.12207972512545902
deviation-heading_max9.88629981298562
deviation-heading_mean2.902386442546943
deviation-heading_median0.7123049320907395
deviation-heading_min0.29863609302067395
driven_any_max17.024626820939837
driven_any_mean5.172321739100612
driven_any_median1.3073280278517272
driven_any_min1.0500040797591588
driven_lanedir_consec_max10.738907315219562
driven_lanedir_consec_mean3.230857353878121
driven_lanedir_consec_min0.6086469169431961
driven_lanedir_max10.738907315219562
driven_lanedir_mean3.230857353878121
driven_lanedir_median0.7879375916748629
driven_lanedir_min0.6086469169431961
get_duckie_state_max4.747334648581112e-06
get_duckie_state_mean4.578952101278567e-06
get_duckie_state_median4.545241888626904e-06
get_duckie_state_min4.477989979279347e-06
get_robot_state_max0.014442358261499647
get_robot_state_mean0.012560278262109922
get_robot_state_median0.012191898870215628
get_robot_state_min0.01141495704650879
get_state_dump_max0.03142377352103209
get_state_dump_mean0.021394682290147692
get_state_dump_median0.019683009736678175
get_state_dump_min0.01478893616620232
get_ui_image_max0.06977898340958816
get_ui_image_mean0.05550555526046111
get_ui_image_median0.055051799379774335
get_ui_image_min0.042139638872707594
in-drivable-lane_max13.399999999999968
in-drivable-lane_mean4.587499999999988
in-drivable-lane_min0.6000000000000005
per-episodes
details{"LF-full-loop-000-ego0": {"driven_any": 17.024626820939837, "get_ui_image": 0.04990483171458372, "step_physics": 0.21928692560126312, "survival_time": 41.049999999999805, "driven_lanedir": 10.738907315219562, "get_state_dump": 0.015113472938537598, "get_robot_state": 0.012077240758278656, "sim_render-ego0": 0.010251507851909257, "get_duckie_state": 4.496017511743699e-06, "in-drivable-lane": 13.399999999999968, "deviation-heading": 9.88629981298562, "agent_compute-ego0": 0.0938885368569924, "complete-iteration": 0.4384466537303878, "set_robot_commands": 0.00809677325896103, "deviation-center-line": 2.241378044644111, "driven_lanedir_consec": 10.738907315219562, "sim_compute_sim_state": 0.02321234932781136, "sim_compute_performance-ego0": 0.006400990080079313}, "LF-full-zigzag-000-ego0": {"driven_any": 1.271625413240163, "get_ui_image": 0.06977898340958816, "step_physics": 0.19659143533462137, "survival_time": 3.8499999999999943, "driven_lanedir": 0.6086469169431961, "get_state_dump": 0.03142377352103209, "get_robot_state": 0.014442358261499647, "sim_render-ego0": 0.010668433629549466, "get_duckie_state": 4.477989979279347e-06, "in-drivable-lane": 2.1499999999999932, "deviation-heading": 0.5902105130620672, "agent_compute-ego0": 0.10827696323394775, "complete-iteration": 0.4731710507319524, "set_robot_commands": 0.010204843985728728, "deviation-center-line": 0.13545176697895162, "driven_lanedir_consec": 0.6086469169431961, "sim_compute_sim_state": 0.02627607186635335, "sim_compute_performance-ego0": 0.005288891303233611}, "LF-full-techtrack-000-ego0": {"driven_any": 1.3430306424632912, "get_ui_image": 0.06019876704496496, "step_physics": 0.17432292769936955, "survival_time": 4.199999999999993, "driven_lanedir": 0.8253719119336291, "get_state_dump": 0.02425254653481876, "get_robot_state": 0.01141495704650879, "sim_render-ego0": 0.01258417578304515, "get_duckie_state": 4.59446626551011e-06, "in-drivable-lane": 2.199999999999992, "deviation-heading": 0.29863609302067395, "agent_compute-ego0": 0.10200898787554571, "complete-iteration": 0.42810838362749887, "set_robot_commands": 0.007692937289967256, "deviation-center-line": 0.12207972512545902, "driven_lanedir_consec": 0.8253719119336291, "sim_compute_sim_state": 0.02786695536445169, "sim_compute_performance-ego0": 0.00753470028147978}, "LF-full-small_loop-000-ego0": {"driven_any": 1.0500040797591588, "get_ui_image": 0.042139638872707594, "step_physics": 0.1608806673218222, "survival_time": 3.349999999999996, "driven_lanedir": 0.7505032714160966, "get_state_dump": 0.01478893616620232, "get_robot_state": 0.012306556982152602, "sim_render-ego0": 0.010185395970064052, "get_duckie_state": 4.747334648581112e-06, "in-drivable-lane": 0.6000000000000005, "deviation-heading": 0.834399351119412, "agent_compute-ego0": 0.09212882378522089, "complete-iteration": 0.3620999069774852, "set_robot_commands": 0.00763858065885656, "deviation-center-line": 0.20914234829283665, "driven_lanedir_consec": 0.7505032714160966, "sim_compute_sim_state": 0.01485281130846809, "sim_compute_performance-ego0": 0.006971850114710191}}
set_robot_commands_max0.010204843985728728
set_robot_commands_mean0.008408283798378394
set_robot_commands_median0.007894855274464143
set_robot_commands_min0.00763858065885656
sim_compute_performance-ego0_max0.00753470028147978
sim_compute_performance-ego0_mean0.006549107944875724
sim_compute_performance-ego0_median0.006686420097394752
sim_compute_performance-ego0_min0.005288891303233611
sim_compute_sim_state_max0.02786695536445169
sim_compute_sim_state_mean0.02305204696677112
sim_compute_sim_state_median0.024744210597082355
sim_compute_sim_state_min0.01485281130846809
sim_render-ego0_max0.01258417578304515
sim_render-ego0_mean0.010922378308641982
sim_render-ego0_median0.010459970740729362
sim_render-ego0_min0.010185395970064052
simulation-passed1
step_physics_max0.21928692560126312
step_physics_mean0.18777048898926907
step_physics_median0.18545718151699545
step_physics_min0.1608806673218222
survival_time_max41.049999999999805
survival_time_mean13.112499999999947
survival_time_min3.349999999999996
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6228913720Frank (Chude) Qian 🇨🇦CBC Net v1aido-LFP_full-sim-validation352successyesreg020:08:51
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survival_time_median6.024999999999986
in-drivable-lane_median2.8249999999999904
driven_lanedir_consec_median0.8269966491210599
deviation-center-line_median0.1259313496227091


other stats
agent_compute-ego0_max0.09563922771187716
agent_compute-ego0_mean0.09079511300773374
agent_compute-ego0_median0.08989148755376183
agent_compute-ego0_min0.08775824921153416
complete-iteration_max0.561736648027287
complete-iteration_mean0.46713720798029806
complete-iteration_median0.47170164318461166
complete-iteration_min0.36340889752468214
deviation-center-line_max0.630121582546472
deviation-center-line_mean0.23068530597329995
deviation-center-line_min0.040756942101309514
deviation-heading_max3.294038201636309
deviation-heading_mean1.289765234924316
deviation-heading_median0.7439285008988457
deviation-heading_min0.3771657362632638
driven_any_max4.21889229608998
driven_any_mean2.0948458544202073
driven_any_median1.8642067941486635
driven_any_min0.4320775332935221
driven_lanedir_consec_max2.981373575777413
driven_lanedir_consec_mean1.2654977234543054
driven_lanedir_consec_min0.42662401979768894
driven_lanedir_max2.981373575777413
driven_lanedir_mean1.2654977234543054
driven_lanedir_median0.8269966491210599
driven_lanedir_min0.42662401979768894
get_duckie_state_max0.07678612282401637
get_duckie_state_mean0.0544892500819881
get_duckie_state_median0.06361064693996257
get_duckie_state_min0.013949583624010888
get_robot_state_max0.012859080967150237
get_robot_state_mean0.01141594907530311
get_robot_state_median0.011187456363895032
get_robot_state_min0.010429802606272144
get_state_dump_max0.02446573390517124
get_state_dump_mean0.021776353936443935
get_state_dump_median0.022223522604065415
get_state_dump_min0.01819263663247367
get_ui_image_max0.06388769815134447
get_ui_image_mean0.054166081834400304
get_ui_image_median0.05473994549029383
get_ui_image_min0.04329673820566908
in-drivable-lane_max4.149999999999985
in-drivable-lane_mean2.4499999999999913
in-drivable-lane_min0.0
per-episodes
details{"LFP-full-loop-000-ego0": {"driven_any": 0.4320775332935221, "get_ui_image": 0.05183640906685277, "step_physics": 0.14851124663102, "survival_time": 1.850000000000001, "driven_lanedir": 0.42662401979768894, "get_state_dump": 0.022768779804832055, "get_robot_state": 0.012859080967150237, "sim_render-ego0": 0.012773739664178146, "get_duckie_state": 0.07678612282401637, "in-drivable-lane": 0.0, "deviation-heading": 0.3771657362632638, "agent_compute-ego0": 0.08885943262200606, "complete-iteration": 0.4549732333735416, "set_robot_commands": 0.011597627087643272, "deviation-center-line": 0.040756942101309514, "driven_lanedir_consec": 0.42662401979768894, "sim_compute_sim_state": 0.02338736935665733, "sim_compute_performance-ego0": 0.005384533028853567}, "LFP-full-zigzag-000-ego0": {"driven_any": 4.21889229608998, "get_ui_image": 0.06388769815134447, "step_physics": 0.2489953029987424, "survival_time": 10.700000000000015, "driven_lanedir": 2.981373575777413, "get_state_dump": 0.02446573390517124, "get_robot_state": 0.010429802606272144, "sim_render-ego0": 0.010167670804400775, "get_duckie_state": 0.06478191530981729, "in-drivable-lane": 2.0999999999999925, "deviation-heading": 3.294038201636309, "agent_compute-ego0": 0.09563922771187716, "complete-iteration": 0.561736648027287, "set_robot_commands": 0.007871475885080737, "deviation-center-line": 0.630121582546472, "driven_lanedir_consec": 2.981373575777413, "sim_compute_sim_state": 0.029934361923572628, "sim_compute_performance-ego0": 0.0053369688433270125}, "LFP-full-techtrack-000-ego0": {"driven_any": 2.097289998344914, "get_ui_image": 0.05764348191373488, "step_physics": 0.1953072951120489, "survival_time": 6.749999999999984, "driven_lanedir": 1.0606168404099838, "get_state_dump": 0.02167826540329877, "get_robot_state": 0.010635204174939324, "sim_render-ego0": 0.009566086180069868, "get_duckie_state": 0.062439378570107854, "in-drivable-lane": 4.149999999999985, "deviation-heading": 0.8779243065232901, "agent_compute-ego0": 0.0909235424855176, "complete-iteration": 0.48843005299568176, "set_robot_commands": 0.006667151170618394, "deviation-center-line": 0.16066097541082722, "driven_lanedir_consec": 1.0606168404099838, "sim_compute_sim_state": 0.027801310314851647, "sim_compute_performance-ego0": 0.00554812655729406}, "LFP-full-small_loop-000-ego0": {"driven_any": 1.6311235899524137, "get_ui_image": 0.04329673820566908, "step_physics": 0.1489993991138779, "survival_time": 5.299999999999989, "driven_lanedir": 0.5933764578321359, "get_state_dump": 0.01819263663247367, "get_robot_state": 0.01173970855285074, "sim_render-ego0": 0.009835067196427103, "get_duckie_state": 0.013949583624010888, "in-drivable-lane": 3.5499999999999883, "deviation-heading": 0.6099326952744012, "agent_compute-ego0": 0.08775824921153416, "complete-iteration": 0.36340889752468214, "set_robot_commands": 0.00705861822466984, "deviation-center-line": 0.09120172383459098, "driven_lanedir_consec": 0.5933764578321359, "sim_compute_sim_state": 0.016355755173157308, "sim_compute_performance-ego0": 0.006012000770212334}}
set_robot_commands_max0.011597627087643272
set_robot_commands_mean0.008298718092003061
set_robot_commands_median0.007465047054875289
set_robot_commands_min0.006667151170618394
sim_compute_performance-ego0_max0.006012000770212334
sim_compute_performance-ego0_mean0.005570407299921743
sim_compute_performance-ego0_median0.005466329793073814
sim_compute_performance-ego0_min0.0053369688433270125
sim_compute_sim_state_max0.029934361923572628
sim_compute_sim_state_mean0.02436969919205973
sim_compute_sim_state_median0.025594339835754487
sim_compute_sim_state_min0.016355755173157308
sim_render-ego0_max0.012773739664178146
sim_render-ego0_mean0.010585640961268974
sim_render-ego0_median0.01000136900041394
sim_render-ego0_min0.009566086180069868
simulation-passed1
step_physics_max0.2489953029987424
step_physics_mean0.1854533109639223
step_physics_median0.1721533471129634
step_physics_min0.14851124663102
survival_time_max10.700000000000015
survival_time_mean6.149999999999999
survival_time_min1.850000000000001
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survival_time_median6.774999999999984
in-drivable-lane_median0.0
driven_lanedir_consec_median2.278087553074077
deviation-center-line_median0.4807952762920742


other stats
agent_compute-ego0_max0.10121784806251526
agent_compute-ego0_mean0.09494033653578732
agent_compute-ego0_median0.09487707093066496
agent_compute-ego0_min0.08878935621930407
complete-iteration_max0.5822893796682964
complete-iteration_mean0.5002246754571094
complete-iteration_median0.5148648651958514
complete-iteration_min0.38887959176843817
deviation-center-line_max1.4589403956416636
deviation-center-line_mean0.627343167131525
deviation-center-line_min0.08884172030028836
deviation-heading_max5.378570894476008
deviation-heading_mean2.276270884533667
deviation-heading_median1.5757416446331307
deviation-heading_min0.5750293543923994
driven_any_max7.60384739383258
driven_any_mean3.192159122063889
driven_any_median2.3572454967009975
driven_any_min0.4502981010209801
driven_lanedir_consec_max5.640815264904232
driven_lanedir_consec_mean2.658801641156088
driven_lanedir_consec_min0.43821619357196706
driven_lanedir_max5.640815264904232
driven_lanedir_mean2.658801641156088
driven_lanedir_median2.278087553074077
driven_lanedir_min0.43821619357196706
get_duckie_state_max0.08295401334762573
get_duckie_state_mean0.05734393332384033
get_duckie_state_median0.06608422520269416
get_duckie_state_min0.0142532695423473
get_robot_state_max0.012852996587753296
get_robot_state_mean0.01235088413082346
get_robot_state_median0.01229501189486137
get_robot_state_min0.011960516145817803
get_state_dump_max0.027388477325439455
get_state_dump_mean0.022820863714309664
get_state_dump_median0.023287211002929235
get_state_dump_min0.017320555525940733
get_ui_image_max0.06482338844668167
get_ui_image_mean0.05696318747863463
get_ui_image_median0.05917992549283164
get_ui_image_min0.044669510482193586
in-drivable-lane_max4.000000000000035
in-drivable-lane_mean1.0000000000000089
in-drivable-lane_min0.0
per-episodes
details{"LFP-full-loop-000-ego0": {"driven_any": 0.4502981010209801, "get_ui_image": 0.057334572076797485, "step_physics": 0.1622810840606689, "survival_time": 1.950000000000001, "driven_lanedir": 0.43821619357196706, "get_state_dump": 0.027388477325439455, "get_robot_state": 0.012852996587753296, "sim_render-ego0": 0.010476064682006837, "get_duckie_state": 0.08295401334762573, "in-drivable-lane": 0.0, "deviation-heading": 0.5750293543923994, "agent_compute-ego0": 0.10121784806251526, "complete-iteration": 0.48843986392021177, "set_robot_commands": 0.00826951265335083, "deviation-center-line": 0.08884172030028836, "driven_lanedir_consec": 0.43821619357196706, "sim_compute_sim_state": 0.018958240747451786, "sim_compute_performance-ego0": 0.006482368707656861}, "LFP-full-zigzag-000-ego0": {"driven_any": 7.60384739383258, "get_ui_image": 0.06482338844668167, "step_physics": 0.2591141617025128, "survival_time": 19.600000000000144, "driven_lanedir": 5.640815264904232, "get_state_dump": 0.023479517482922586, "get_robot_state": 0.011960516145817803, "sim_render-ego0": 0.01080367704687531, "get_duckie_state": 0.06922192245949316, "in-drivable-lane": 4.000000000000035, "deviation-heading": 5.378570894476008, "agent_compute-ego0": 0.09617683905681582, "complete-iteration": 0.5822893796682964, "set_robot_commands": 0.007355242285109659, "deviation-center-line": 1.4589403956416636, "driven_lanedir_consec": 5.640815264904232, "sim_compute_sim_state": 0.032359386521744665, "sim_compute_performance-ego0": 0.006767213496239737}, "LFP-full-techtrack-000-ego0": {"driven_any": 2.027674569714446, "get_ui_image": 0.06102527890886579, "step_physics": 0.2353677409035819, "survival_time": 5.899999999999987, "driven_lanedir": 1.9497400554267788, "get_state_dump": 0.023094904522935885, "get_robot_state": 0.012342210577315644, "sim_render-ego0": 0.008655327708781267, "get_duckie_state": 0.06294652794589516, "in-drivable-lane": 0.0, "deviation-heading": 1.4055795151056212, "agent_compute-ego0": 0.09357730280451414, "complete-iteration": 0.5412898664714909, "set_robot_commands": 0.008679391957130753, "deviation-center-line": 0.4391191575503109, "driven_lanedir_consec": 1.9497400554267788, "sim_compute_sim_state": 0.029546415104585538, "sim_compute_performance-ego0": 0.00582171488208931}, "LFP-full-small_loop-000-ego0": {"driven_any": 2.6868164236875485, "get_ui_image": 0.044669510482193586, "step_physics": 0.17044262452559036, "survival_time": 7.649999999999981, "driven_lanedir": 2.606435050721375, "get_state_dump": 0.017320555525940733, "get_robot_state": 0.0122478132124071, "sim_render-ego0": 0.010663269402144793, "get_duckie_state": 0.0142532695423473, "in-drivable-lane": 0.0, "deviation-heading": 1.7459037741606402, "agent_compute-ego0": 0.08878935621930407, "complete-iteration": 0.38887959176843817, "set_robot_commands": 0.007512682444089419, "deviation-center-line": 0.5224713950338374, "driven_lanedir_consec": 2.606435050721375, "sim_compute_sim_state": 0.015731874998513754, "sim_compute_performance-ego0": 0.007026749771910828}}
set_robot_commands_max0.008679391957130753
set_robot_commands_mean0.007954207334920164
set_robot_commands_median0.007891097548720125
set_robot_commands_min0.007355242285109659
sim_compute_performance-ego0_max0.007026749771910828
sim_compute_performance-ego0_mean0.006524511714474184
sim_compute_performance-ego0_median0.006624791101948298
sim_compute_performance-ego0_min0.00582171488208931
sim_compute_sim_state_max0.032359386521744665
sim_compute_sim_state_mean0.02414897934307393
sim_compute_sim_state_median0.024252327926018655
sim_compute_sim_state_min0.015731874998513754
sim_render-ego0_max0.01080367704687531
sim_render-ego0_mean0.010149584709952052
sim_render-ego0_median0.010569667042075811
sim_render-ego0_min0.008655327708781267
simulation-passed1
step_physics_max0.2591141617025128
step_physics_mean0.2068014027980885
step_physics_median0.20290518271458613
step_physics_min0.1622810840606689
survival_time_max19.600000000000144
survival_time_mean8.775000000000029
survival_time_min1.950000000000001
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driven_lanedir_consec_median28.770455989580604
survival_time_median59.99999999999873
deviation-center-line_median2.5276460017285243
in-drivable-lane_median0.0


other stats
agent_compute-ego0_max0.03894159537767192
agent_compute-ego0_mean0.0346000954868593
agent_compute-ego0_median0.03371647643010682
agent_compute-ego0_min0.03202583370955163
complete-iteration_max0.4596279431739318
complete-iteration_mean0.3984132732181724
complete-iteration_median0.40063228684599256
complete-iteration_min0.3327605760067726
deviation-center-line_max2.6336778637309397
deviation-center-line_mean2.334584824550065
deviation-center-line_min1.6493694310122735
deviation-heading_max8.019650448742077
deviation-heading_mean7.2023007494639915
deviation-heading_median7.343658708102941
deviation-heading_min6.102235132908008
driven_any_max30.972736379731742
driven_any_mean29.425421807233526
driven_any_median29.034047566629088
driven_any_min28.66085571594418
driven_lanedir_consec_max30.61801965515254
driven_lanedir_consec_mean29.1190053344364
driven_lanedir_consec_min28.31708970343186
driven_lanedir_max30.61801965515254
driven_lanedir_mean29.1190053344364
driven_lanedir_median28.770455989580604
driven_lanedir_min28.31708970343186
get_duckie_state_max3.751767465812181e-06
get_duckie_state_mean3.2565675111337066e-06
get_duckie_state_median3.1537358508717504e-06
get_duckie_state_min2.967030876979145e-06
get_robot_state_max0.015217837644159349
get_robot_state_mean0.013178180893890864
get_robot_state_median0.01278751954547968
get_robot_state_min0.01191984684044475
get_state_dump_max0.02389870059182503
get_state_dump_mean0.01908654287196913
get_state_dump_median0.01907422113775909
get_state_dump_min0.014299028620533304
get_ui_image_max0.06250298152259744
get_ui_image_mean0.05378632442242497
get_ui_image_median0.05405138057038548
get_ui_image_min0.044539555026331515
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 30.972736379731742, "get_ui_image": 0.05189087527876194, "step_physics": 0.19513519657938605, "survival_time": 59.99999999999873, "driven_lanedir": 30.61801965515254, "get_state_dump": 0.02389870059182503, "get_robot_state": 0.015217837644159349, "sim_render-ego0": 0.011205360950974998, "get_duckie_state": 2.967030876979145e-06, "in-drivable-lane": 0.0, "deviation-heading": 7.1091503763884445, "agent_compute-ego0": 0.03894159537767192, "complete-iteration": 0.37890622994981144, "set_robot_commands": 0.009243262598258473, "deviation-center-line": 2.555440262768427, "driven_lanedir_consec": 30.61801965515254, "sim_compute_sim_state": 0.025543088023608967, "sim_compute_performance-ego0": 0.007622441086145761}, "LF-norm-zigzag-000-ego0": {"driven_any": 28.69556498806268, "get_ui_image": 0.06250298152259744, "step_physics": 0.27703158682728685, "survival_time": 59.99999999999873, "driven_lanedir": 28.31708970343186, "get_state_dump": 0.01797840259752107, "get_robot_state": 0.012615224503160615, "sim_render-ego0": 0.010434326780130228, "get_duckie_state": 3.751767465812181e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.019650448742077, "agent_compute-ego0": 0.03202583370955163, "complete-iteration": 0.4596279431739318, "set_robot_commands": 0.00789400302400994, "deviation-center-line": 2.4998517406886216, "driven_lanedir_consec": 28.31708970343186, "sim_compute_sim_state": 0.03214480815382425, "sim_compute_performance-ego0": 0.006795602277554044}, "LF-norm-techtrack-000-ego0": {"driven_any": 29.372530145195498, "get_ui_image": 0.056211885862009016, "step_physics": 0.24049922091875545, "survival_time": 59.99999999999873, "driven_lanedir": 29.050999186927157, "get_state_dump": 0.020170039677997117, "get_robot_state": 0.012959814587798742, "sim_render-ego0": 0.01039627211774815, "get_duckie_state": 3.110558464564848e-06, "in-drivable-lane": 0.0, "deviation-heading": 7.578167039817438, "agent_compute-ego0": 0.035182890348093, "complete-iteration": 0.4223583437421737, "set_robot_commands": 0.008234767492962916, "deviation-center-line": 2.6336778637309397, "driven_lanedir_consec": 29.050999186927157, "sim_compute_sim_state": 0.03157020131316808, "sim_compute_performance-ego0": 0.0069148564318832414}, "LF-norm-small_loop-000-ego0": {"driven_any": 28.66085571594418, "get_ui_image": 0.044539555026331515, "step_physics": 0.18923699548103529, "survival_time": 59.99999999999873, "driven_lanedir": 28.489912792234048, "get_state_dump": 0.014299028620533304, "get_robot_state": 0.01191984684044475, "sim_render-ego0": 0.009578888064915692, "get_duckie_state": 3.196913237178653e-06, "in-drivable-lane": 0.0, "deviation-heading": 6.102235132908008, "agent_compute-ego0": 0.032250062512120634, "complete-iteration": 0.3327605760067726, "set_robot_commands": 0.0077329970319304836, "deviation-center-line": 1.6493694310122735, "driven_lanedir_consec": 28.489912792234048, "sim_compute_sim_state": 0.01698791434822432, "sim_compute_performance-ego0": 0.00600220480131964}}
set_robot_commands_max0.009243262598258473
set_robot_commands_mean0.008276257536790453
set_robot_commands_median0.008064385258486427
set_robot_commands_min0.0077329970319304836
sim_compute_performance-ego0_max0.007622441086145761
sim_compute_performance-ego0_mean0.0068337761492256715
sim_compute_performance-ego0_median0.0068552293547186425
sim_compute_performance-ego0_min0.00600220480131964
sim_compute_sim_state_max0.03214480815382425
sim_compute_sim_state_mean0.026561502959706405
sim_compute_sim_state_median0.028556644668388524
sim_compute_sim_state_min0.01698791434822432
sim_render-ego0_max0.011205360950974998
sim_render-ego0_mean0.010403711978442266
sim_render-ego0_median0.010415299448939187
sim_render-ego0_min0.009578888064915692
simulation-passed1
step_physics_max0.27703158682728685
step_physics_mean0.22547574995161593
step_physics_median0.2178172087490707
step_physics_min0.18923699548103529
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
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6226913550András Kalapos 🇭🇺real-v0.9-3092-363aido-LF-sim-validation347successyesreg020:59:01
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driven_lanedir_consec_median27.212188486805196
survival_time_median59.99999999999873
deviation-center-line_median2.62676217493598
in-drivable-lane_median0.0


other stats
agent_compute-ego0_max0.03353529607723595
agent_compute-ego0_mean0.03254528104216729
agent_compute-ego0_median0.03249515016112697
agent_compute-ego0_min0.03165552776917927
complete-iteration_max0.4571869561912416
complete-iteration_mean0.38963690874082263
complete-iteration_median0.3856349245495443
complete-iteration_min0.33009082967296033
deviation-center-line_max2.932511547707182
deviation-center-line_mean2.5941046265155183
deviation-center-line_min2.190382608482932
deviation-heading_max8.427061044071385
deviation-heading_mean6.693100728696232
deviation-heading_median6.4702035125765915
deviation-heading_min5.40493484556036
driven_any_max28.544109959532868
driven_any_mean27.58993952637571
driven_any_median27.45047824301168
driven_any_min26.914691659946595
driven_lanedir_consec_max28.38493863519559
driven_lanedir_consec_mean27.382363538733344
driven_lanedir_consec_min26.720138546127387
driven_lanedir_max28.38493863519559
driven_lanedir_mean27.382363538733344
driven_lanedir_median27.212188486805196
driven_lanedir_min26.720138546127387
get_duckie_state_max2.963854609480706e-06
get_duckie_state_mean2.916111338644798e-06
get_duckie_state_median2.925143849343483e-06
get_duckie_state_min2.850303046411519e-06
get_robot_state_max0.012690703735859765
get_robot_state_mean0.012091052968932826
get_robot_state_median0.012169817702954852
get_robot_state_min0.01133387273396183
get_state_dump_max0.01895506832621477
get_state_dump_mean0.017747191118261004
get_state_dump_median0.017827152610321428
get_state_dump_min0.01637939092618639
get_ui_image_max0.06305981158813172
get_ui_image_mean0.05448211619101596
get_ui_image_median0.05422077716935386
get_ui_image_min0.04642709883722437
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 28.544109959532868, "get_ui_image": 0.05193700163092443, "step_physics": 0.19412544188551065, "survival_time": 59.99999999999873, "driven_lanedir": 28.38493863519559, "get_state_dump": 0.01895506832621477, "get_robot_state": 0.012690703735859765, "sim_render-ego0": 0.010178296393299976, "get_duckie_state": 2.9271290165300077e-06, "in-drivable-lane": 0.0, "deviation-heading": 5.40493484556036, "agent_compute-ego0": 0.032621991525184704, "complete-iteration": 0.35923256147513283, "set_robot_commands": 0.0077917831922748705, "deviation-center-line": 2.190382608482932, "driven_lanedir_consec": 28.38493863519559, "sim_compute_sim_state": 0.02423768039547732, "sim_compute_performance-ego0": 0.0065021125799809565}, "LF-norm-zigzag-000-ego0": {"driven_any": 26.914691659946595, "get_ui_image": 0.06305981158813172, "step_physics": 0.27352220152538087, "survival_time": 59.99999999999873, "driven_lanedir": 26.720138546127387, "get_state_dump": 0.01828465354531929, "get_robot_state": 0.012363203757013708, "sim_render-ego0": 0.009849981304807132, "get_duckie_state": 2.923158682156959e-06, "in-drivable-lane": 0.0, "deviation-heading": 6.542717789450528, "agent_compute-ego0": 0.03353529607723595, "complete-iteration": 0.4571869561912416, "set_robot_commands": 0.007942230278605923, "deviation-center-line": 2.452255038352363, "driven_lanedir_consec": 26.720138546127387, "sim_compute_sim_state": 0.032015177729127806, "sim_compute_performance-ego0": 0.006419741045326913}, "LF-norm-techtrack-000-ego0": {"driven_any": 27.18064622401334, "get_ui_image": 0.05650455270778329, "step_physics": 0.24223287992930032, "survival_time": 59.99999999999873, "driven_lanedir": 26.989472796761536, "get_state_dump": 0.01637939092618639, "get_robot_state": 0.011976431648896, "sim_render-ego0": 0.010298287838722248, "get_duckie_state": 2.850303046411519e-06, "in-drivable-lane": 0.0, "deviation-heading": 6.397689235702655, "agent_compute-ego0": 0.032368308797069235, "complete-iteration": 0.41203728762395575, "set_robot_commands": 0.007201063345115846, "deviation-center-line": 2.932511547707182, "driven_lanedir_consec": 26.989472796761536, "sim_compute_sim_state": 0.02917540123023955, "sim_compute_performance-ego0": 0.005712798394132514}, "LF-norm-small_loop-000-ego0": {"driven_any": 27.72031026201002, "get_ui_image": 0.04642709883722437, "step_physics": 0.18314919860833492, "survival_time": 59.99999999999873, "driven_lanedir": 27.43490417684886, "get_state_dump": 0.017369651675323564, "get_robot_state": 0.01133387273396183, "sim_render-ego0": 0.010067184402186308, "get_duckie_state": 2.963854609480706e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.427061044071385, "agent_compute-ego0": 0.03165552776917927, "complete-iteration": 0.33009082967296033, "set_robot_commands": 0.00736519121111283, "deviation-center-line": 2.8012693115195972, "driven_lanedir_consec": 27.43490417684886, "sim_compute_sim_state": 0.01634284796861685, "sim_compute_performance-ego0": 0.006187021484184424}}
set_robot_commands_max0.007942230278605923
set_robot_commands_mean0.007575067006777367
set_robot_commands_median0.00757848720169385
set_robot_commands_min0.007201063345115846
sim_compute_performance-ego0_max0.0065021125799809565
sim_compute_performance-ego0_mean0.006205418375906202
sim_compute_performance-ego0_median0.006303381264755669
sim_compute_performance-ego0_min0.005712798394132514
sim_compute_sim_state_max0.032015177729127806
sim_compute_sim_state_mean0.025442776830865384
sim_compute_sim_state_median0.02670654081285843
sim_compute_sim_state_min0.01634284796861685
sim_render-ego0_max0.010298287838722248
sim_render-ego0_mean0.010098437484753916
sim_render-ego0_median0.010122740397743142
sim_render-ego0_min0.009849981304807132
simulation-passed1
step_physics_max0.27352220152538087
step_physics_mean0.22325743048713168
step_physics_median0.2181791609074055
step_physics_min0.18314919860833492
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
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6226513611Raphael Jeanmobile-segmentation-pedestrianaido-LF-sim-validation347successyesreg021:11:12
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driven_lanedir_consec_median25.99055509912304
survival_time_median59.99999999999873
deviation-center-line_median3.227842224441896
in-drivable-lane_median0.29999999999998295


other stats
agent_compute-ego0_max0.2068780500823314
agent_compute-ego0_mean0.1616767798236367
agent_compute-ego0_median0.2024070100919293
agent_compute-ego0_min0.0350150490283569
complete-iteration_max0.6598866972498453
complete-iteration_mean0.5476804211375914
complete-iteration_median0.5426496183941705
complete-iteration_min0.4455357505121795
deviation-center-line_max3.684422663876693
deviation-center-line_mean2.9857185131203123
deviation-center-line_min1.8027669397207648
deviation-heading_max10.48757317919206
deviation-heading_mean9.53899936312267
deviation-heading_median10.040804787357429
deviation-heading_min7.586814698583766
driven_any_max27.53710913592516
driven_any_mean26.829308881564373
driven_any_median26.671985515825945
driven_any_min26.43615535868045
driven_lanedir_consec_max27.19551493750692
driven_lanedir_consec_mean25.87262346708257
driven_lanedir_consec_min24.31386873257729
driven_lanedir_max27.19551493750692
driven_lanedir_mean25.87262346708257
driven_lanedir_median25.99055509912304
driven_lanedir_min24.31386873257729
get_duckie_state_max2.9322904512149707e-06
get_duckie_state_mean2.849856383794551e-06
get_duckie_state_median2.873033210697222e-06
get_duckie_state_min2.7210686625687904e-06
get_robot_state_max0.0132142145568187
get_robot_state_mean0.012198721340157209
get_robot_state_median0.012124011459001195
get_robot_state_min0.011332647885807746
get_state_dump_max0.020410148031407848
get_state_dump_mean0.017719021248479967
get_state_dump_median0.01758550823380012
get_state_dump_min0.015294920494911771
get_ui_image_max0.06388565364427908
get_ui_image_mean0.054764835115872657
get_ui_image_median0.05416108736090616
get_ui_image_min0.0468515120973992
in-drivable-lane_max3.44999999999997
in-drivable-lane_mean1.012499999999984
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 27.53710913592516, "get_ui_image": 0.05374435481183436, "step_physics": 0.224322155850019, "survival_time": 59.99999999999873, "driven_lanedir": 27.19551493750692, "get_state_dump": 0.020410148031407848, "get_robot_state": 0.0132142145568187, "sim_render-ego0": 0.0114597273706695, "get_duckie_state": 2.8219151556442224e-06, "in-drivable-lane": 0.0, "deviation-heading": 7.586814698583766, "agent_compute-ego0": 0.20618894058500695, "complete-iteration": 0.572006070742897, "set_robot_commands": 0.00935278347787214, "deviation-center-line": 1.8027669397207648, "driven_lanedir_consec": 27.19551493750692, "sim_compute_sim_state": 0.02557184932432405, "sim_compute_performance-ego0": 0.007518960474730531}, "LF-norm-zigzag-000-ego0": {"driven_any": 26.683901460624178, "get_ui_image": 0.06388565364427908, "step_physics": 0.29939999548620627, "survival_time": 59.99999999999873, "driven_lanedir": 25.87390914743265, "get_state_dump": 0.01748304422650111, "get_robot_state": 0.01226708950547751, "sim_render-ego0": 0.011127067743788948, "get_duckie_state": 2.9322904512149707e-06, "in-drivable-lane": 0.5999999999999659, "deviation-heading": 9.676336862644302, "agent_compute-ego0": 0.2068780500823314, "complete-iteration": 0.6598866972498453, "set_robot_commands": 0.008314710969631121, "deviation-center-line": 3.014977503645415, "driven_lanedir_consec": 25.87390914743265, "sim_compute_sim_state": 0.03325263566518207, "sim_compute_performance-ego0": 0.007047340137376873}, "LF-norm-techtrack-000-ego0": {"driven_any": 26.43615535868045, "get_ui_image": 0.054577819909977975, "step_physics": 0.2791117897240149, "survival_time": 59.99999999999873, "driven_lanedir": 24.31386873257729, "get_state_dump": 0.015294920494911771, "get_robot_state": 0.011332647885807746, "sim_render-ego0": 0.009903962765009973, "get_duckie_state": 2.7210686625687904e-06, "in-drivable-lane": 3.44999999999997, "deviation-heading": 10.48757317919206, "agent_compute-ego0": 0.0350150490283569, "complete-iteration": 0.4455357505121795, "set_robot_commands": 0.006689399207859214, "deviation-center-line": 3.440706945238377, "driven_lanedir_consec": 24.31386873257729, "sim_compute_sim_state": 0.02764714488776697, "sim_compute_performance-ego0": 0.005736178502155085}, "LF-norm-small_loop-000-ego0": {"driven_any": 26.660069571027712, "get_ui_image": 0.0468515120973992, "step_physics": 0.19583722951509475, "survival_time": 59.99999999999873, "driven_lanedir": 26.10720105081343, "get_state_dump": 0.017687972241099133, "get_robot_state": 0.01198093341252488, "sim_render-ego0": 0.01068732641221681, "get_duckie_state": 2.9241512657502213e-06, "in-drivable-lane": 0.0, "deviation-heading": 10.405272712070552, "agent_compute-ego0": 0.19862507959885164, "complete-iteration": 0.5132931660454438, "set_robot_commands": 0.00865624111756794, "deviation-center-line": 3.684422663876693, "driven_lanedir_consec": 26.10720105081343, "sim_compute_sim_state": 0.01637914000105401, "sim_compute_performance-ego0": 0.006357289273772609}}
set_robot_commands_max0.00935278347787214
set_robot_commands_mean0.008253283693232603
set_robot_commands_median0.00848547604359953
set_robot_commands_min0.006689399207859214
sim_compute_performance-ego0_max0.007518960474730531
sim_compute_performance-ego0_mean0.006664942097008774
sim_compute_performance-ego0_median0.006702314705574741
sim_compute_performance-ego0_min0.005736178502155085
sim_compute_sim_state_max0.03325263566518207
sim_compute_sim_state_mean0.025712692469581775
sim_compute_sim_state_median0.02660949710604551
sim_compute_sim_state_min0.01637914000105401
sim_render-ego0_max0.0114597273706695
sim_render-ego0_mean0.010794521072921306
sim_render-ego0_median0.01090719707800288
sim_render-ego0_min0.009903962765009973
simulation-passed1
step_physics_max0.29939999548620627
step_physics_mean0.24966779264383376
step_physics_median0.25171697278701693
step_physics_min0.19583722951509475
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
No reset possible
6226113632Raphael Jeanmobile-segmentationaido-LFP-sim-validation350successyesreg020:07:55
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survival_time_median4.7749999999999915
in-drivable-lane_median0.0
driven_lanedir_consec_median1.6540628337251968
deviation-center-line_median0.2397796923052304


other stats
agent_compute-ego0_max0.212291382728739
agent_compute-ego0_mean0.17876970723387642
agent_compute-ego0_median0.20492634695399303
agent_compute-ego0_min0.0929347522987807
complete-iteration_max0.6688484435385846
complete-iteration_mean0.6013916794586958
complete-iteration_median0.6081870767203243
complete-iteration_min0.5203441208555498
deviation-center-line_max0.49607251562928933
deviation-center-line_mean0.27026079948182746
deviation-center-line_min0.10541129768755962
deviation-heading_max1.397148742176067
deviation-heading_mean0.9124148710125736
deviation-heading_median0.8729895002548466
deviation-heading_min0.5065317413645336
driven_any_max2.5146002636483153
driven_any_mean1.62345917349445
driven_any_median1.7218132658317664
driven_any_min0.5356098986659518
driven_lanedir_consec_max2.4665678012704655
driven_lanedir_consec_mean1.5747255102224766
driven_lanedir_consec_min0.5242085721690475
driven_lanedir_max2.4665678012704655
driven_lanedir_mean1.5747255102224766
driven_lanedir_median1.6540628337251968
driven_lanedir_min0.5242085721690475
get_duckie_state_max0.09049097762620154
get_duckie_state_mean0.056278849074513346
get_duckie_state_median0.060557564381448374
get_duckie_state_min0.013509289908955117
get_robot_state_max0.013030629512692286
get_robot_state_mean0.01174733729436703
get_robot_state_median0.012043307189682134
get_robot_state_min0.009872105285411572
get_state_dump_max0.028484257784756748
get_state_dump_mean0.023617672630925342
get_state_dump_median0.025665889077998223
get_state_dump_min0.014654654582948175
get_ui_image_max0.06456590206065077
get_ui_image_mean0.05853844467366303
get_ui_image_median0.05960039630244914
get_ui_image_min0.05038708402910305
in-drivable-lane_max0.09999999999999964
in-drivable-lane_mean0.02499999999999991
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.2293482549599073, "get_ui_image": 0.05616442034067201, "step_physics": 0.23116918634777223, "survival_time": 5.999999999999987, "driven_lanedir": 2.122829695042795, "get_state_dump": 0.028484257784756748, "get_robot_state": 0.013030629512692286, "sim_render-ego0": 0.011256428789501349, "get_duckie_state": 0.09049097762620154, "in-drivable-lane": 0.09999999999999964, "deviation-heading": 1.397148742176067, "agent_compute-ego0": 0.0929347522987807, "complete-iteration": 0.5580047910863702, "set_robot_commands": 0.008192564830307133, "deviation-center-line": 0.35094070877799066, "driven_lanedir_consec": 2.122829695042795, "sim_compute_sim_state": 0.019824104860794445, "sim_compute_performance-ego0": 0.0062084158590017275}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5356098986659518, "get_ui_image": 0.06456590206065077, "step_physics": 0.25455744723056223, "survival_time": 2.3, "driven_lanedir": 0.5242085721690475, "get_state_dump": 0.024944772111608626, "get_robot_state": 0.011212425029024165, "sim_render-ego0": 0.008401439545002389, "get_duckie_state": 0.056547271444442423, "in-drivable-lane": 0.0, "deviation-heading": 0.5065317413645336, "agent_compute-ego0": 0.212291382728739, "complete-iteration": 0.6688484435385846, "set_robot_commands": 0.010186575828714574, "deviation-center-line": 0.12861867583247014, "driven_lanedir_consec": 0.5242085721690475, "sim_compute_sim_state": 0.020925526923321666, "sim_compute_performance-ego0": 0.004981015590911216}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.2142782767036258, "get_ui_image": 0.06303637226422627, "step_physics": 0.22985612021552193, "survival_time": 3.5499999999999954, "driven_lanedir": 1.1852959724075984, "get_state_dump": 0.026387006044387817, "get_robot_state": 0.012874189350340102, "sim_render-ego0": 0.009640074438518949, "get_duckie_state": 0.06456785731845432, "in-drivable-lane": 0.0, "deviation-heading": 0.5661313522019644, "agent_compute-ego0": 0.207333419058058, "complete-iteration": 0.6583693623542786, "set_robot_commands": 0.008851120869318644, "deviation-center-line": 0.10541129768755962, "driven_lanedir_consec": 1.1852959724075984, "sim_compute_sim_state": 0.028469459878073797, "sim_compute_performance-ego0": 0.007121026515960693}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.5146002636483153, "get_ui_image": 0.05038708402910305, "step_physics": 0.1914163054400728, "survival_time": 6.499999999999985, "driven_lanedir": 2.4665678012704655, "get_state_dump": 0.014654654582948175, "get_robot_state": 0.009872105285411572, "sim_render-ego0": 0.008848756324243909, "get_duckie_state": 0.013509289908955117, "in-drivable-lane": 0.0, "deviation-heading": 1.1798476483077287, "agent_compute-ego0": 0.20251927484992807, "complete-iteration": 0.5203441208555498, "set_robot_commands": 0.008278688401666307, "deviation-center-line": 0.49607251562928933, "driven_lanedir_consec": 2.4665678012704655, "sim_compute_sim_state": 0.014624177044584552, "sim_compute_performance-ego0": 0.006017508397575553}}
set_robot_commands_max0.010186575828714574
set_robot_commands_mean0.008877237482501663
set_robot_commands_median0.008564904635492476
set_robot_commands_min0.008192564830307133
sim_compute_performance-ego0_max0.007121026515960693
sim_compute_performance-ego0_mean0.006081991590862298
sim_compute_performance-ego0_median0.00611296212828864
sim_compute_performance-ego0_min0.004981015590911216
sim_compute_sim_state_max0.028469459878073797
sim_compute_sim_state_mean0.02096081717669361
sim_compute_sim_state_median0.020374815892058053
sim_compute_sim_state_min0.014624177044584552
sim_render-ego0_max0.011256428789501349
sim_render-ego0_mean0.00953667477431665
sim_render-ego0_median0.009244415381381429
sim_render-ego0_min0.008401439545002389
simulation-passed1
step_physics_max0.25455744723056223
step_physics_mean0.2267497648084823
step_physics_median0.2305126532816471
step_physics_min0.1914163054400728
survival_time_max6.499999999999985
survival_time_mean4.587499999999992
survival_time_min2.3
No reset possible
6225813634Raphael Jeanmobile-segmentationaido-LFV-sim-validation354abortednoreg020:07:42
KeyboardInterrupt: T [...]
KeyboardInterrupt:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 1169, in run_one
    heartbeat()
  File "/usr/local/lib/python3.8/dist-packages/duckietown_challenges_runner/runner.py", line 628, in heartbeat
    raise KeyboardInterrupt(msg_)
KeyboardInterrupt: The server told us to abort the job because: The challenge has been updated.
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6225513641Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LF-sim-validation347failednoreg020:03:09
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6224613640Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LF-sim-testing348failednoreg020:03:56
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
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No reset possible
6223813729YU CHENBC Net V2aido-LF-sim-validation347successnoreg020:30:20
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driven_lanedir_consec_median4.245814041421714
survival_time_median20.550000000000157
deviation-center-line_median0.8979199372543613
in-drivable-lane_median9.225000000000076


other stats
agent_compute-ego0_max0.09632753646435516
agent_compute-ego0_mean0.09368068409559097
agent_compute-ego0_median0.09429746713576811
agent_compute-ego0_min0.08980026564647242
complete-iteration_max0.4761873559864689
complete-iteration_mean0.4270159077514718
complete-iteration_median0.4199450292764835
complete-iteration_min0.39198621646645143
deviation-center-line_max4.02331688046553
deviation-center-line_mean1.515693839832002
deviation-center-line_min0.2436186043537546
deviation-heading_max19.328873988776504
deviation-heading_mean7.4035635228130126
deviation-heading_median4.589654388320891
deviation-heading_min1.106071325833763
driven_any_max24.956531448998955
driven_any_mean11.117350116420647
driven_any_median8.008107064330135
driven_any_min3.4966548880233663
driven_lanedir_consec_max16.604217844348764
driven_lanedir_consec_mean6.619817130097297
driven_lanedir_consec_min1.383422593196992
driven_lanedir_max16.604217844348764
driven_lanedir_mean6.619817130097297
driven_lanedir_median4.245814041421714
driven_lanedir_min1.383422593196992
get_duckie_state_max2.93946636773144e-06
get_duckie_state_mean2.829116130284486e-06
get_duckie_state_median2.811604565658955e-06
get_duckie_state_min2.753789022088595e-06
get_robot_state_max0.012504544432304766
get_robot_state_mean0.011471787120832256
get_robot_state_median0.011504982059834964
get_robot_state_min0.010372639931354326
get_state_dump_max0.02140767715837313
get_state_dump_mean0.0186378303176964
get_state_dump_median0.01863558588605689
get_state_dump_min0.015872472340298684
get_ui_image_max0.06197452708466413
get_ui_image_mean0.05217134586817256
get_ui_image_median0.05053772319681936
get_ui_image_min0.04563540999438741
in-drivable-lane_max14.949999999999642
in-drivable-lane_mean9.93749999999995
in-drivable-lane_min6.350000000000007
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 3.4966548880233663, "get_ui_image": 0.046710782444354185, "step_physics": 0.18005050826318486, "survival_time": 9.650000000000002, "driven_lanedir": 1.383422593196992, "get_state_dump": 0.015872472340298684, "get_robot_state": 0.010372639931354326, "sim_render-ego0": 0.009742358296187885, "get_duckie_state": 2.7651639328789464e-06, "in-drivable-lane": 6.350000000000007, "deviation-heading": 1.106071325833763, "agent_compute-ego0": 0.08980026564647242, "complete-iteration": 0.39198621646645143, "set_robot_commands": 0.00860180928535068, "deviation-center-line": 0.2436186043537546, "driven_lanedir_consec": 1.383422593196992, "sim_compute_sim_state": 0.024518867128903103, "sim_compute_performance-ego0": 0.006099759917898276}, "LF-norm-zigzag-000-ego0": {"driven_any": 8.358746604832792, "get_ui_image": 0.06197452708466413, "step_physics": 0.22653006745255705, "survival_time": 21.850000000000176, "driven_lanedir": 4.946478213636219, "get_state_dump": 0.02140767715837313, "get_robot_state": 0.012504544432304766, "sim_render-ego0": 0.010152849432540266, "get_duckie_state": 2.753789022088595e-06, "in-drivable-lane": 8.85000000000008, "deviation-heading": 5.1519261486046375, "agent_compute-ego0": 0.09491199282206356, "complete-iteration": 0.4761873559864689, "set_robot_commands": 0.009377881816533058, "deviation-center-line": 0.9831246926762204, "driven_lanedir_consec": 4.946478213636219, "sim_compute_sim_state": 0.03230716703144927, "sim_compute_performance-ego0": 0.006804921311330578}, "LF-norm-techtrack-000-ego0": {"driven_any": 7.657467523827478, "get_ui_image": 0.05436466394928453, "step_physics": 0.2144593667489877, "survival_time": 19.25000000000014, "driven_lanedir": 3.5451498692072096, "get_state_dump": 0.01841926265874675, "get_robot_state": 0.010995905016370388, "sim_render-ego0": 0.009987805173804725, "get_duckie_state": 2.93946636773144e-06, "in-drivable-lane": 9.600000000000072, "deviation-heading": 4.027382628037145, "agent_compute-ego0": 0.09632753646435516, "complete-iteration": 0.4462278599566129, "set_robot_commands": 0.007095346796697904, "deviation-center-line": 0.8127151818325022, "driven_lanedir_consec": 3.5451498692072096, "sim_compute_sim_state": 0.02793753579490543, "sim_compute_performance-ego0": 0.006420901402290621}, "LF-norm-small_loop-000-ego0": {"driven_any": 24.956531448998955, "get_ui_image": 0.04563540999438741, "step_physics": 0.17881523977211372, "survival_time": 59.99999999999873, "driven_lanedir": 16.604217844348764, "get_state_dump": 0.018851909113367035, "get_robot_state": 0.012014059103299538, "sim_render-ego0": 0.01083697228507138, "get_duckie_state": 2.8580451984389637e-06, "in-drivable-lane": 14.949999999999642, "deviation-heading": 19.328873988776504, "agent_compute-ego0": 0.09368294144947265, "complete-iteration": 0.3936621985963541, "set_robot_commands": 0.008862987942342257, "deviation-center-line": 4.02331688046553, "driven_lanedir_consec": 16.604217844348764, "sim_compute_sim_state": 0.01744266830812783, "sim_compute_performance-ego0": 0.007302516703800198}}
set_robot_commands_max0.009377881816533058
set_robot_commands_mean0.008484506460230974
set_robot_commands_median0.008732398613846469
set_robot_commands_min0.007095346796697904
sim_compute_performance-ego0_max0.007302516703800198
sim_compute_performance-ego0_mean0.0066570248338299175
sim_compute_performance-ego0_median0.006612911356810599
sim_compute_performance-ego0_min0.006099759917898276
sim_compute_sim_state_max0.03230716703144927
sim_compute_sim_state_mean0.02555155956584641
sim_compute_sim_state_median0.026228201461904264
sim_compute_sim_state_min0.01744266830812783
sim_render-ego0_max0.01083697228507138
sim_render-ego0_mean0.010179996296901065
sim_render-ego0_median0.010070327303172494
sim_render-ego0_min0.009742358296187885
simulation-passed1
step_physics_max0.22653006745255705
step_physics_mean0.19996379555921084
step_physics_median0.19725493750608628
step_physics_min0.17881523977211372
survival_time_max59.99999999999873
survival_time_mean27.68749999999976
survival_time_min9.650000000000002
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6222713720Frank (Chude) Qian 🇨🇦CBC Net v1aido-LFP_full-sim-validation352successnoreg020:14:30
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survival_time_median6.024999999999986
in-drivable-lane_median3.849999999999987
driven_lanedir_consec_median0.8269966491210599
deviation-center-line_median0.1259313496227091


other stats
agent_compute-ego0_max0.12153128573769018
agent_compute-ego0_mean0.1051707122793856
agent_compute-ego0_median0.10072210468005838
agent_compute-ego0_min0.09770735401973546
complete-iteration_max0.646097051952532
complete-iteration_mean0.515534469512983
complete-iteration_median0.5153173330952139
complete-iteration_min0.385406159908972
deviation-center-line_max0.5383158061502973
deviation-center-line_mean0.20773386187425627
deviation-center-line_min0.040756942101309514
deviation-heading_max3.22775290105804
deviation-heading_mean1.273193909779749
deviation-heading_median0.7439285008988457
deviation-heading_min0.3771657362632638
driven_any_max4.922030254610177
driven_any_mean2.2706303440502564
driven_any_median1.8642067941486635
driven_any_min0.4320775332935221
driven_lanedir_consec_max2.9177023230435233
driven_lanedir_consec_mean1.249579910270833
driven_lanedir_consec_min0.42662401979768894
driven_lanedir_max2.9177023230435233
driven_lanedir_mean1.249579910270833
driven_lanedir_median0.8269966491210599
driven_lanedir_min0.42662401979768894
get_duckie_state_max0.0916655000887419
get_duckie_state_mean0.0662278954163305
get_duckie_state_median0.07947717807525168
get_duckie_state_min0.01429172542607673
get_robot_state_max0.01700198409045756
get_robot_state_mean0.014277460874113995
get_robot_state_median0.013923619160555996
get_robot_state_min0.01226062108488644
get_state_dump_max0.040923014343508826
get_state_dump_mean0.029725138143480084
get_state_dump_median0.02941308684946952
get_state_dump_min0.019151364531472464
get_ui_image_max0.08009786065290814
get_ui_image_mean0.061119005942003826
get_ui_image_median0.05849392126219191
get_ui_image_min0.04739032059072334
in-drivable-lane_max4.800000000000033
in-drivable-lane_mean3.1250000000000018
in-drivable-lane_min0.0
per-episodes
details{"LFP-full-loop-000-ego0": {"driven_any": 0.4320775332935221, "get_ui_image": 0.05587937957362125, "step_physics": 0.14922241160744115, "survival_time": 1.850000000000001, "driven_lanedir": 0.42662401979768894, "get_state_dump": 0.02886087643472772, "get_robot_state": 0.015444354007118627, "sim_render-ego0": 0.01221323013305664, "get_duckie_state": 0.0916655000887419, "in-drivable-lane": 0.0, "deviation-heading": 0.3771657362632638, "agent_compute-ego0": 0.1018299993715788, "complete-iteration": 0.49560678005218506, "set_robot_commands": 0.01217940606568989, "deviation-center-line": 0.040756942101309514, "driven_lanedir_consec": 0.42662401979768894, "sim_compute_sim_state": 0.02240242456134997, "sim_compute_performance-ego0": 0.005642025094283254}, "LFP-full-zigzag-000-ego0": {"driven_any": 4.922030254610177, "get_ui_image": 0.08009786065290814, "step_physics": 0.2251736760622094, "survival_time": 12.30000000000004, "driven_lanedir": 2.9177023230435233, "get_state_dump": 0.040923014343508826, "get_robot_state": 0.01700198409045756, "sim_render-ego0": 0.013810944460664202, "get_duckie_state": 0.08329953741930757, "in-drivable-lane": 4.800000000000033, "deviation-heading": 3.22775290105804, "agent_compute-ego0": 0.12153128573769018, "complete-iteration": 0.646097051952532, "set_robot_commands": 0.009264271268960437, "deviation-center-line": 0.5383158061502973, "driven_lanedir_consec": 2.9177023230435233, "sim_compute_sim_state": 0.04142301671418101, "sim_compute_performance-ego0": 0.013315968185301253}, "LFP-full-techtrack-000-ego0": {"driven_any": 2.097289998344914, "get_ui_image": 0.06110846295076258, "step_physics": 0.1968984603881836, "survival_time": 6.749999999999984, "driven_lanedir": 1.0606168404099838, "get_state_dump": 0.02996529726421132, "get_robot_state": 0.01226062108488644, "sim_render-ego0": 0.01267184755381416, "get_duckie_state": 0.07565481873119578, "in-drivable-lane": 4.149999999999985, "deviation-heading": 0.8779243065232901, "agent_compute-ego0": 0.09961420998853796, "complete-iteration": 0.5350278861382428, "set_robot_commands": 0.008137748521917006, "deviation-center-line": 0.16066097541082722, "driven_lanedir_consec": 1.0606168404099838, "sim_compute_sim_state": 0.03127038829466876, "sim_compute_performance-ego0": 0.007195432396496043}, "LFP-full-small_loop-000-ego0": {"driven_any": 1.6311235899524137, "get_ui_image": 0.04739032059072334, "step_physics": 0.1485051507147673, "survival_time": 5.299999999999989, "driven_lanedir": 0.5933764578321359, "get_state_dump": 0.019151364531472464, "get_robot_state": 0.012402884313993364, "sim_render-ego0": 0.010959342261341131, "get_duckie_state": 0.01429172542607673, "in-drivable-lane": 3.5499999999999883, "deviation-heading": 0.6099326952744012, "agent_compute-ego0": 0.09770735401973546, "complete-iteration": 0.385406159908972, "set_robot_commands": 0.00907792332016419, "deviation-center-line": 0.09120172383459098, "driven_lanedir_consec": 0.5933764578321359, "sim_compute_sim_state": 0.01927279534740983, "sim_compute_performance-ego0": 0.00641079929387458}}
set_robot_commands_max0.01217940606568989
set_robot_commands_mean0.009664837294182878
set_robot_commands_median0.009171097294562312
set_robot_commands_min0.008137748521917006
sim_compute_performance-ego0_max0.013315968185301253
sim_compute_performance-ego0_mean0.008141056242488781
sim_compute_performance-ego0_median0.006803115845185312
sim_compute_performance-ego0_min0.005642025094283254
sim_compute_sim_state_max0.04142301671418101
sim_compute_sim_state_mean0.028592156229402393
sim_compute_sim_state_median0.026836406428009364
sim_compute_sim_state_min0.01927279534740983
sim_render-ego0_max0.013810944460664202
sim_render-ego0_mean0.012413841102219034
sim_render-ego0_median0.0124425388434354
sim_render-ego0_min0.010959342261341131
simulation-passed1
step_physics_max0.2251736760622094
step_physics_mean0.17994992469315035
step_physics_median0.17306043599781237
step_physics_min0.1485051507147673
survival_time_max12.30000000000004
survival_time_mean6.550000000000004
survival_time_min1.850000000000001
No reset possible
6221113703Frank (Chude) Qian 🇨🇦BC Net V2aido-LF-sim-validation347host-errornoreg020:00:44
Error while running [...]
Error while running Docker Compose:

Could not run command
│    cmd: [docker-compose, -p, reg02_c3f15323bf18-job62211-973871, up, -d]
│ stdout: ''
│  sderr: ''
│      e: Command '['docker-compose', '-p', 'reg02_c3f15323bf18-job62211-973871', 'up', '-d']' returned non-zero exit status 1.
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6220113702Frank (Chude) Qian 🇨🇦BC Net V2aido-LF-sim-validation347host-errornoreg020:00:41
Error while running [...]
Error while running Docker Compose:

Could not run command
│    cmd: [docker-compose, -p, reg02_c3f15323bf18-job62201-595204, up, -d]
│ stdout: ''
│  sderr: ''
│      e: Command '['docker-compose', '-p', 'reg02_c3f15323bf18-job62201-595204', 'up', '-d']' returned non-zero exit status 1.
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6218913700Frank (Chude) Qian 🇨🇦BC Net V2aido-LF-sim-validation347host-errornoreg020:01:01
InvalidEnvironment: [...]
InvalidEnvironment:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 45, in init
              ||     limit_gpu_memory()
              ||   File "solution.py", line 29, in limit_gpu_memory
              ||     logical_gpus = tf.config.experimental.list_logical_devices('GPU')
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py", line 439, in list_logical_devices
              ||     return context.context().list_logical_devices(device_type=device_type)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 1368, in list_logical_devices
              ||     self.ensure_initialized()
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 526, in ensure_initialized
              ||     context_handle = pywrap_tfe.TFE_NewContext(opts)
              || tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 45, in init
              || |     limit_gpu_memory()
              || |   File "solution.py", line 29, in limit_gpu_memory
              || |     logical_gpus = tf.config.experimental.list_logical_devices('GPU')
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py", line 439, in list_logical_devices
              || |     return context.context().list_logical_devices(device_type=device_type)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 1368, in list_logical_devices
              || |     self.ensure_initialized()
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 526, in ensure_initialized
              || |     context_handle = pywrap_tfe.TFE_NewContext(opts)
              || | tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 248, in main
    raise InvalidEnvironment(msg) from e
duckietown_challenges.exceptions.InvalidEnvironment: Detected out of CUDA memory:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 45, in init
              ||     limit_gpu_memory()
              ||   File "solution.py", line 29, in limit_gpu_memory
              ||     logical_gpus = tf.config.experimental.list_logical_devices('GPU')
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py", line 439, in list_logical_devices
              ||     return context.context().list_logical_devices(device_type=device_type)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 1368, in list_logical_devices
              ||     self.ensure_initialized()
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 526, in ensure_initialized
              ||     context_handle = pywrap_tfe.TFE_NewContext(opts)
              || tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 45, in init
              || |     limit_gpu_memory()
              || |   File "solution.py", line 29, in limit_gpu_memory
              || |     logical_gpus = tf.config.experimental.list_logical_devices('GPU')
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/config.py", line 439, in list_logical_devices
              || |     return context.context().list_logical_devices(device_type=device_type)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 1368, in list_logical_devices
              || |     self.ensure_initialized()
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/context.py", line 526, in ensure_initialized
              || |     context_handle = pywrap_tfe.TFE_NewContext(opts)
              || | tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
              || |
              ||
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6217513693Samuel Alexandertemplate-rosaido-LF-sim-validation347successnoreg020:13:02
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driven_lanedir_consec_median0.6170081810984177
survival_time_median12.900000000000048
deviation-center-line_median0.19867033894208289
in-drivable-lane_median10.850000000000044


other stats
agent_compute-ego0_max0.04748881500934868
agent_compute-ego0_mean0.03934346562952996
agent_compute-ego0_median0.037730363037028286
agent_compute-ego0_min0.03442432143471458
complete-iteration_max0.4058677708661115
complete-iteration_mean0.3495487716705381
complete-iteration_median0.3401206875974625
complete-iteration_min0.31208594062111594
deviation-center-line_max0.33444273289483856
deviation-center-line_mean0.20853035767774153
deviation-center-line_min0.1023380199319619
deviation-heading_max2.206274583283399
deviation-heading_mean1.108270987468961
deviation-heading_median0.9019194175941796
deviation-heading_min0.4229705314040847
driven_any_max4.484792123383148
driven_any_mean3.30912612601694
driven_any_median3.5056399493754555
driven_any_min1.7404324819336991
driven_lanedir_consec_max0.9153504279687236
driven_lanedir_consec_mean0.6158449558212293
driven_lanedir_consec_min0.31401303311935846
driven_lanedir_max0.9153504279687236
driven_lanedir_mean0.6158449558212293
driven_lanedir_median0.6170081810984177
driven_lanedir_min0.31401303311935846
get_duckie_state_max4.776174371892755e-06
get_duckie_state_mean4.401781483075568e-06
get_duckie_state_median4.4188428235311786e-06
get_duckie_state_min3.993265913347158e-06
get_robot_state_max0.018130810663042736
get_robot_state_mean0.014526047093304932
get_robot_state_median0.013501632943510372
get_robot_state_min0.012970111823156244
get_state_dump_max0.03690777394016093
get_state_dump_mean0.027004495214641253
get_state_dump_median0.02606705143782864
get_state_dump_min0.018976104042746803
get_ui_image_max0.06699116337937092
get_ui_image_mean0.05934539704714793
get_ui_image_median0.060135388414742245
get_ui_image_min0.05011964797973633
in-drivable-lane_max11.900000000000077
in-drivable-lane_mean9.425000000000036
in-drivable-lane_min4.099999999999985
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 3.749454658576709, "get_ui_image": 0.05011964797973633, "step_physics": 0.1377577816356312, "survival_time": 13.70000000000006, "driven_lanedir": 0.31401303311935846, "get_state_dump": 0.018976104042746803, "get_robot_state": 0.013989491029219196, "sim_render-ego0": 0.012703969261863016, "get_duckie_state": 4.776174371892755e-06, "in-drivable-lane": 11.75000000000005, "deviation-heading": 1.2716347528774994, "agent_compute-ego0": 0.03442432143471458, "complete-iteration": 0.31208594062111594, "set_robot_commands": 0.008991431322964755, "deviation-center-line": 0.20842715942172263, "driven_lanedir_consec": 0.31401303311935846, "sim_compute_sim_state": 0.027294653979214756, "sim_compute_performance-ego0": 0.007600339542735706}, "LF-norm-zigzag-000-ego0": {"driven_any": 4.484792123383148, "get_ui_image": 0.06570732110757323, "step_physics": 0.16585874557495117, "survival_time": 16.000000000000092, "driven_lanedir": 0.9153504279687236, "get_state_dump": 0.021219622680331316, "get_robot_state": 0.012970111823156244, "sim_render-ego0": 0.01106545486925547, "get_duckie_state": 4.493558889608888e-06, "in-drivable-lane": 11.900000000000077, "deviation-heading": 2.206274583283399, "agent_compute-ego0": 0.03724333578923781, "complete-iteration": 0.35799420659787184, "set_robot_commands": 0.008112259000261253, "deviation-center-line": 0.33444273289483856, "driven_lanedir_consec": 0.9153504279687236, "sim_compute_sim_state": 0.02873935283530167, "sim_compute_performance-ego0": 0.006852416605964256}, "LF-norm-techtrack-000-ego0": {"driven_any": 3.2618252401742023, "get_ui_image": 0.06699116337937092, "step_physics": 0.17123718909275384, "survival_time": 12.100000000000035, "driven_lanedir": 0.5575978328545987, "get_state_dump": 0.03690777394016093, "get_robot_state": 0.018130810663042736, "sim_render-ego0": 0.01524354989636582, "get_duckie_state": 3.993265913347158e-06, "in-drivable-lane": 9.950000000000037, "deviation-heading": 0.4229705314040847, "agent_compute-ego0": 0.04748881500934868, "complete-iteration": 0.4058677708661115, "set_robot_commands": 0.011419612193794408, "deviation-center-line": 0.1023380199319619, "driven_lanedir_consec": 0.5575978328545987, "sim_compute_sim_state": 0.029149501902576334, "sim_compute_performance-ego0": 0.00910089732197577}, "LF-norm-small_loop-000-ego0": {"driven_any": 1.7404324819336991, "get_ui_image": 0.054563455721911264, "step_physics": 0.1429958133136525, "survival_time": 6.749999999999984, "driven_lanedir": 0.6764185293422367, "get_state_dump": 0.030914480195325965, "get_robot_state": 0.013013774857801547, "sim_render-ego0": 0.011384387226665722, "get_duckie_state": 4.344126757453469e-06, "in-drivable-lane": 4.099999999999985, "deviation-heading": 0.5322040823108597, "agent_compute-ego0": 0.03821739028481876, "complete-iteration": 0.3222471685970531, "set_robot_commands": 0.008285012315301335, "deviation-center-line": 0.18891351846244311, "driven_lanedir_consec": 0.6764185293422367, "sim_compute_sim_state": 0.014489978551864624, "sim_compute_performance-ego0": 0.008165787248050465}}
set_robot_commands_max0.011419612193794408
set_robot_commands_mean0.009202078708080435
set_robot_commands_median0.008638221819133044
set_robot_commands_min0.008112259000261253
sim_compute_performance-ego0_max0.00910089732197577
sim_compute_performance-ego0_mean0.00792986017968155
sim_compute_performance-ego0_median0.007883063395393086
sim_compute_performance-ego0_min0.006852416605964256
sim_compute_sim_state_max0.029149501902576334
sim_compute_sim_state_mean0.024918371817239347
sim_compute_sim_state_median0.028017003407258212
sim_compute_sim_state_min0.014489978551864624
sim_render-ego0_max0.01524354989636582
sim_render-ego0_mean0.012599340313537506
sim_render-ego0_median0.012044178244264368
sim_render-ego0_min0.01106545486925547
simulation-passed1
step_physics_max0.17123718909275384
step_physics_mean0.15446238240424717
step_physics_median0.15442727944430185
step_physics_min0.1377577816356312
survival_time_max16.000000000000092
survival_time_mean12.137500000000044
survival_time_min6.749999999999984
No reset possible
6215313520András Kalapos 🇭🇺real-v1.0-3092-363aido-LF-sim-validation347successnoreg021:00:27
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
driven_lanedir_consec_median28.748751507312075
survival_time_median59.99999999999873
deviation-center-line_median2.5724207885512937
in-drivable-lane_median0.0


other stats
agent_compute-ego0_max0.03913137934587083
agent_compute-ego0_mean0.03757990275096337
agent_compute-ego0_median0.0377152597378136
agent_compute-ego0_min0.03575771218235546
complete-iteration_max0.4954770663497251
complete-iteration_mean0.42139782317969127
complete-iteration_median0.4151885262337652
complete-iteration_min0.3597371739015095
deviation-center-line_max2.7523743463120263
deviation-center-line_mean2.587040772902735
deviation-center-line_min2.450947168196327
deviation-heading_max8.420386860169318
deviation-heading_mean7.816185866648665
deviation-heading_median8.037323070462028
deviation-heading_min6.769710465501283
driven_any_max31.329441138588056
driven_any_mean29.58887695113551
driven_any_median29.055947730000703
driven_any_min28.91417120595259
driven_lanedir_consec_max31.05287959229932
driven_lanedir_consec_mean29.282881041952407
driven_lanedir_consec_min28.581141560886156
driven_lanedir_max31.05287959229932
driven_lanedir_mean29.282881041952407
driven_lanedir_median28.748751507312075
driven_lanedir_min28.581141560886156
get_duckie_state_max3.0434598136603286e-06
get_duckie_state_mean2.9487177096834586e-06
get_duckie_state_median2.9533332233921276e-06
get_duckie_state_min2.8447445782892514e-06
get_robot_state_max0.014487023357546994
get_robot_state_mean0.013358195258814726
get_robot_state_median0.01311044629467814
get_robot_state_min0.01272486508835563
get_state_dump_max0.024856955681514185
get_state_dump_mean0.021683288900977267
get_state_dump_median0.022086802966191706
get_state_dump_min0.017702593990011478
get_ui_image_max0.06728830563833474
get_ui_image_mean0.05721263151978771
get_ui_image_median0.05637995418561289
get_ui_image_min0.04880231206959034
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 31.329441138588056, "get_ui_image": 0.05518793920791715, "step_physics": 0.1999310492278138, "survival_time": 59.99999999999873, "driven_lanedir": 31.05287959229932, "get_state_dump": 0.024856955681514185, "get_robot_state": 0.014487023357546994, "sim_render-ego0": 0.012931759411051909, "get_duckie_state": 2.871147301870024e-06, "in-drivable-lane": 0.0, "deviation-heading": 6.769710465501283, "agent_compute-ego0": 0.03913137934587083, "complete-iteration": 0.39090350009718106, "set_robot_commands": 0.009187968942545336, "deviation-center-line": 2.4622569895758932, "driven_lanedir_consec": 31.05287959229932, "sim_compute_sim_state": 0.026123258096788646, "sim_compute_performance-ego0": 0.008850842490978384}, "LF-norm-zigzag-000-ego0": {"driven_any": 28.91417120595259, "get_ui_image": 0.06728830563833474, "step_physics": 0.2883200500529573, "survival_time": 59.99999999999873, "driven_lanedir": 28.581141560886156, "get_state_dump": 0.024400286233792395, "get_robot_state": 0.01341988105361011, "sim_render-ego0": 0.011095571478240992, "get_duckie_state": 2.8447445782892514e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.073324683850567, "agent_compute-ego0": 0.03756093303924993, "complete-iteration": 0.4954770663497251, "set_robot_commands": 0.009727548103745437, "deviation-center-line": 2.6825845875266947, "driven_lanedir_consec": 28.581141560886156, "sim_compute_sim_state": 0.03615273246161646, "sim_compute_performance-ego0": 0.007297154965746115}, "LF-norm-techtrack-000-ego0": {"driven_any": 29.13106880151358, "get_ui_image": 0.05757196916330863, "step_physics": 0.25829340754500235, "survival_time": 59.99999999999873, "driven_lanedir": 28.791485027955627, "get_state_dump": 0.017702593990011478, "get_robot_state": 0.012801011535746172, "sim_render-ego0": 0.010605248086756214, "get_duckie_state": 3.035519144914231e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.420386860169318, "agent_compute-ego0": 0.03575771218235546, "complete-iteration": 0.43947355237034935, "set_robot_commands": 0.008181052045163069, "deviation-center-line": 2.7523743463120263, "driven_lanedir_consec": 28.791485027955627, "sim_compute_sim_state": 0.03146972684042341, "sim_compute_performance-ego0": 0.006863809842848957}, "LF-norm-small_loop-000-ego0": {"driven_any": 28.98082665848783, "get_ui_image": 0.04880231206959034, "step_physics": 0.19538009275901724, "survival_time": 59.99999999999873, "driven_lanedir": 28.706017986668524, "get_state_dump": 0.01977331969859102, "get_robot_state": 0.01272486508835563, "sim_render-ego0": 0.011258089175132989, "get_duckie_state": 3.0434598136603286e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.00132145707349, "agent_compute-ego0": 0.03786958643637728, "complete-iteration": 0.3597371739015095, "set_robot_commands": 0.008976066837104333, "deviation-center-line": 2.450947168196327, "driven_lanedir_consec": 28.706017986668524, "sim_compute_sim_state": 0.017841232904883646, "sim_compute_performance-ego0": 0.006894218435295417}}
set_robot_commands_max0.009727548103745437
set_robot_commands_mean0.009018158982139543
set_robot_commands_median0.009082017889824837
set_robot_commands_min0.008181052045163069
sim_compute_performance-ego0_max0.008850842490978384
sim_compute_performance-ego0_mean0.007476506433717218
sim_compute_performance-ego0_median0.007095686700520766
sim_compute_performance-ego0_min0.006863809842848957
sim_compute_sim_state_max0.03615273246161646
sim_compute_sim_state_mean0.02789673757592804
sim_compute_sim_state_median0.02879649246860603
sim_compute_sim_state_min0.017841232904883646
sim_render-ego0_max0.012931759411051909
sim_render-ego0_mean0.011472667037795526
sim_render-ego0_median0.011176830326686989
sim_render-ego0_min0.010605248086756214
simulation-passed1
step_physics_max0.2883200500529573
step_physics_mean0.23548114989619767
step_physics_median0.22911222838640807
step_physics_min0.19538009275901724
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
No reset possible
6215113533András Kalapos 🇭🇺real-v1.0-3092-363aido-LFV_multi-sim-validation356failednoreg020:02:01
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego3" aborted with the following error:

error in ego3 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6215013533András Kalapos 🇭🇺real-v1.0-3092-363aido-LFV_multi-sim-validation356failednoreg020:01:51
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego2" aborted with the following error:

error in ego2 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
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No reset possible
6214913533András Kalapos 🇭🇺real-v1.0-3092-363aido-LFV_multi-sim-validation356failednoreg020:01:50
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego2" aborted with the following error:

error in ego2 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6214813533András Kalapos 🇭🇺real-v1.0-3092-363aido-LFV_multi-sim-validation356failednoreg020:01:53
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego2" aborted with the following error:

error in ego2 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6213613543András Kalapos 🇭🇺3090aido-LFV-sim-validation354successnoreg020:34:21
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
survival_time_median9.025000000000002
in-drivable-lane_median0.0
driven_lanedir_consec_median3.765417408969266
deviation-center-line_median0.36259646839214565


other stats
agent_compute-ego0_max0.0432952344417572
agent_compute-ego0_mean0.03902076261230731
agent_compute-ego0_median0.03777982799517668
agent_compute-ego0_min0.03722816001711868
agent_compute-npc0_max0.0936167190472285
agent_compute-npc0_mean0.07722077011203592
agent_compute-npc0_median0.07573362054556526
agent_compute-npc0_min0.06379912030978466
complete-iteration_max1.918228191970497
complete-iteration_mean1.2441419248635497
complete-iteration_median1.2319440043430714
complete-iteration_min0.5944514987975593
deviation-center-line_max0.8497875756869387
deviation-center-line_mean0.4421434052738346
deviation-center-line_min0.19359310862410825
deviation-heading_max3.5353859709105926
deviation-heading_mean1.8054298316024509
deviation-heading_median1.4519620883572426
deviation-heading_min0.7824091787847257
driven_any_max10.419551457414084
driven_any_mean5.1503998131731725
driven_any_median3.845621726253982
driven_any_min2.4908043427706414
driven_lanedir_consec_max10.242822595198248
driven_lanedir_consec_mean5.059628727761386
driven_lanedir_consec_min2.464857497908768
driven_lanedir_max10.242822595198248
driven_lanedir_mean5.059628727761386
driven_lanedir_median3.765417408969266
driven_lanedir_min2.464857497908768
get_duckie_state_max4.154112603929308e-06
get_duckie_state_mean3.7719633707077625e-06
get_duckie_state_median3.869278228305756e-06
get_duckie_state_min3.195184422290231e-06
get_robot_state_max0.06537755894404586
get_robot_state_mean0.04886912699610234
get_robot_state_median0.05299806583508328
get_robot_state_min0.024102817370196965
get_state_dump_max0.04116136166784498
get_state_dump_mean0.03221720039862498
get_state_dump_median0.03322780376015153
get_state_dump_min0.021251832406351884
get_ui_image_max0.09430213487276468
get_ui_image_mean0.07914698272201437
get_ui_image_median0.08347025708702238
get_ui_image_min0.05534528184124804
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LFV-norm-loop-000-ego0": {"driven_any": 4.657202210694511, "get_ui_image": 0.07525447732237377, "step_physics": 0.45129919814192543, "survival_time": 10.90000000000002, "driven_lanedir": 4.52550601628898, "get_state_dump": 0.03082211376869515, "get_robot_state": 0.05044334773059305, "sim_render-ego0": 0.010409611000862296, "sim_render-npc0": 0.011362912992364195, "sim_render-npc1": 0.010108332655745554, "sim_render-npc2": 0.011254615435317228, "get_duckie_state": 3.7961898873385777e-06, "in-drivable-lane": 0.0, "deviation-heading": 2.0911957738022418, "agent_compute-ego0": 0.03785454628130072, "agent_compute-npc0": 0.07526403583892405, "agent_compute-npc1": 0.07237573628011904, "agent_compute-npc2": 0.09096466787329548, "complete-iteration": 1.0389756470510405, "set_robot_commands": 0.008169352736102935, "deviation-center-line": 0.4606236571753992, "driven_lanedir_consec": 4.52550601628898, "sim_compute_sim_state": 0.05072907334593333, "sim_compute_performance-ego0": 0.0069400785176177, "sim_compute_performance-npc0": 0.0067312902511527, "sim_compute_performance-npc1": 0.007877561055361953, "sim_compute_performance-npc2": 0.007360495388780011}, "LFV-norm-zigzag-000-ego0": {"driven_any": 10.419551457414084, "get_ui_image": 0.09430213487276468, "step_physics": 1.127491444926108, "survival_time": 23.200000000000195, "driven_lanedir": 10.242822595198248, "get_state_dump": 0.03563349375160792, "get_robot_state": 0.06537755894404586, "sim_render-ego0": 0.01124402323076802, "sim_render-npc0": 0.011653690440680393, "sim_render-npc1": 0.013346087035312449, "sim_render-npc2": 0.011884093540970996, "sim_render-npc3": 0.011757417904433383, "get_duckie_state": 3.942366569272933e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.5353859709105926, "agent_compute-ego0": 0.03770510970905263, "agent_compute-npc0": 0.07620320525220646, "agent_compute-npc1": 0.07688631652503886, "agent_compute-npc2": 0.08179335286540369, "agent_compute-npc3": 0.0742281247210759, "complete-iteration": 1.918228191970497, "set_robot_commands": 0.008699169466572423, "deviation-center-line": 0.8497875756869387, "driven_lanedir_consec": 10.242822595198248, "sim_compute_sim_state": 0.10797091863488638, "sim_compute_performance-ego0": 0.007210238261889386, "sim_compute_performance-npc0": 0.006600552220498362, "sim_compute_performance-npc1": 0.007159324358868343, "sim_compute_performance-npc2": 0.006549871608775149, "sim_compute_performance-npc3": 0.006828616255073137}, "LFV-norm-techtrack-000-ego0": {"driven_any": 3.034041241813453, "get_ui_image": 0.091686036851671, "step_physics": 0.5993329998519685, "survival_time": 7.149999999999983, "driven_lanedir": 3.0053288016495516, "get_state_dump": 0.04116136166784498, "get_robot_state": 0.0555527839395735, "sim_render-ego0": 0.011618543002340527, "sim_render-npc0": 0.01076247129175398, "sim_render-npc1": 0.012304662002457513, "sim_render-npc2": 0.010436856084399752, "sim_render-npc3": 0.012486489282713996, "get_duckie_state": 4.154112603929308e-06, "in-drivable-lane": 0.0, "deviation-heading": 0.7824091787847257, "agent_compute-ego0": 0.0432952344417572, "agent_compute-npc0": 0.0936167190472285, "agent_compute-npc1": 0.08734687666098277, "agent_compute-npc2": 0.08740336034033033, "agent_compute-npc3": 0.09314467675156064, "complete-iteration": 1.4249123616351025, "set_robot_commands": 0.009884716735945808, "deviation-center-line": 0.26456927960889215, "driven_lanedir_consec": 3.0053288016495516, "sim_compute_sim_state": 0.1014891349607044, "sim_compute_performance-ego0": 0.006659484571880764, "sim_compute_performance-npc0": 0.006586997045411004, "sim_compute_performance-npc1": 0.007951706647872925, "sim_compute_performance-npc2": 0.005752301878399319, "sim_compute_performance-npc3": 0.0058842963642544216}, "LFV-norm-small_loop-000-ego0": {"driven_any": 2.4908043427706414, "get_ui_image": 0.05534528184124804, "step_physics": 0.3144517470532515, "survival_time": 6.299999999999986, "driven_lanedir": 2.464857497908768, "get_state_dump": 0.021251832406351884, "get_robot_state": 0.024102817370196965, "sim_render-ego0": 0.01165673676438219, "sim_render-npc0": 0.010453368735125684, "get_duckie_state": 3.195184422290231e-06, "in-drivable-lane": 0.0, "deviation-heading": 0.8127284029122434, "agent_compute-ego0": 0.03722816001711868, "agent_compute-npc0": 0.06379912030978466, "complete-iteration": 0.5944514987975593, "set_robot_commands": 0.009400422178854154, "deviation-center-line": 0.19359310862410825, "driven_lanedir_consec": 2.464857497908768, "sim_compute_sim_state": 0.02543041086572362, "sim_compute_performance-ego0": 0.006614885930939922, "sim_compute_performance-npc0": 0.006523962095966489}}
set_robot_commands_max0.009884716735945808
set_robot_commands_mean0.00903841527936883
set_robot_commands_median0.00904979582271329
set_robot_commands_min0.008169352736102935
sim_compute_performance-ego0_max0.007210238261889386
sim_compute_performance-ego0_mean0.006856171820581944
sim_compute_performance-ego0_median0.006799781544749232
sim_compute_performance-ego0_min0.006614885930939922
sim_compute_performance-npc0_max0.0067312902511527
sim_compute_performance-npc0_mean0.006610700403257139
sim_compute_performance-npc0_median0.006593774632954683
sim_compute_performance-npc0_min0.006523962095966489
sim_compute_sim_state_max0.10797091863488638
sim_compute_sim_state_mean0.07140488445181194
sim_compute_sim_state_median0.07610910415331885
sim_compute_sim_state_min0.02543041086572362
sim_render-ego0_max0.01165673676438219
sim_render-ego0_mean0.011232228499588258
sim_render-ego0_median0.011431283116554274
sim_render-ego0_min0.010409611000862296
sim_render-npc0_max0.011653690440680393
sim_render-npc0_mean0.011058110864981062
sim_render-npc0_median0.01106269214205909
sim_render-npc0_min0.010453368735125684
simulation-passed1
step_physics_max1.127491444926108
step_physics_mean0.6231438474933133
step_physics_median0.525316098996947
step_physics_min0.3144517470532515
survival_time_max23.200000000000195
survival_time_mean11.887500000000044
survival_time_min6.299999999999986
No reset possible
6213513544András Kalapos 🇭🇺3090aido-LFVI-sim-testing360failednoreg020:02:56
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 126, in <module>
              ||     main()
              ||   File "solution.py", line 122, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 126, in <module>
              || |     main()
              || |   File "solution.py", line 122, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
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6213413544András Kalapos 🇭🇺3090aido-LFVI-sim-testing360failednoreg020:02:07
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 126, in <module>
              ||     main()
              ||   File "solution.py", line 122, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 126, in <module>
              || |     main()
              || |   File "solution.py", line 122, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6213313544András Kalapos 🇭🇺3090aido-LFVI-sim-testing360failednoreg020:02:07
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 126, in <module>
              ||     main()
              ||   File "solution.py", line 122, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 126, in <module>
              || |     main()
              || |   File "solution.py", line 122, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6213213545András Kalapos 🇭🇺3090aido-LFVI-sim-validation359failednoreg020:02:22
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 126, in <module>
              ||     main()
              ||   File "solution.py", line 122, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 126, in <module>
              || |     main()
              || |   File "solution.py", line 122, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
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6213113546András Kalapos 🇭🇺3090aido-LFVI_multi-sim-validation365failednoreg020:03:24
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
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6213013546András Kalapos 🇭🇺3090aido-LFVI_multi-sim-validation365failednoreg020:03:28
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
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6212913547András Kalapos 🇭🇺3090aido-LFV_multi-sim-testing357failednoreg020:03:45
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
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6212813548András Kalapos 🇭🇺3090aido-LFV_multi-sim-validation356failednoreg020:03:38
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
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6212713548András Kalapos 🇭🇺3090aido-LFV_multi-sim-validation356failednoreg020:03:28
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
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6212613549András Kalapos 🇭🇺real-v0.9-3092-363aido-LF-sim-testing348failednoreg020:01:16
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6212513549András Kalapos 🇭🇺real-v0.9-3092-363aido-LF-sim-testing348failednoreg020:01:12
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6212413549András Kalapos 🇭🇺real-v0.9-3092-363aido-LF-sim-testing348failednoreg020:01:14
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
Artefacts hidden. If you are the author, please login using the top-right link or use the dashboard.
No reset possible
6212313549András Kalapos 🇭🇺real-v0.9-3092-363aido-LF-sim-testing348failednoreg020:01:15
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego0" aborted with the following error:

error in ego0 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              ||     return fn(*args)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              ||     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              ||     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || During handling of the above exception, another exception occurred:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              ||     return session_or_none.run(symbolic_out[0], feed_dict)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              ||     result = self._run(None, fetches, feed_dict, options_ptr,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              ||     results = self._do_run(handle, final_targets, final_fetches,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              ||     return self._do_call(_run_fn, feeds, fetches, targets, options,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              ||     raise type(e)(node_def, op, message)
              || tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              ||   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              ||   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || 	 [[default_policy/strided_slice_1/_3]]
              || 0 successful operations.
              || 0 derived errors ignored.
              ||
              || Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              ||   File "solution.py", line 127, in <module>
              ||     main()
              ||   File "solution.py", line 123, in main
              ||     wrap_direct(node=node, protocol=protocol)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              ||     run_loop(node, protocol, args)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              ||     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              ||   File "/submission/model.py", line 55, in __init__
              ||     self.model = PPOTrainer(config=config["rllib_config"])
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              ||     Trainer.__init__(self, config, env, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              ||     super().__init__(config, logger_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              ||     self._setup(copy.deepcopy(self.config))
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              ||     self._init(self.config, self.env_creator)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              ||     self.workers = self._make_workers(env_creator, self._policy,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              ||     return WorkerSet(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              ||     self._local_worker = self._make_worker(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              ||     worker = cls(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              ||     self._build_policy_map(policy_dict, policy_config)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              ||     policy_map[name] = cls(obs_space, act_space, merged_conf)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              ||     DynamicTFPolicy.__init__(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              ||     self._initialize_loss()
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              ||     postprocessed_batch = self.postprocess_trajectory(
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              ||     return postprocess_fn(self, sample_batch, other_agent_batches,
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              ||     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              ||     symbolic_out[0] = fn(*placeholders)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              ||     model_out, _ = self.model({
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              ||     res = self.forward(restored, state or [], seq_lens)
              ||   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              ||     model_out, self._value_out = self.base_model(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              ||     return self._run_internal_graph(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              ||     outputs = node.layer(*args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              ||     outputs = call_fn(cast_inputs, *args, **kwargs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              ||     return self.activation(outputs)
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              ||     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              ||     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              ||     ret = Operation(
              ||   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              ||     self._traceback = tf_stack.extract_stack()
              ||
              ||
              || The above exception was the direct cause of the following exception:
              ||
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 339, in loop
              ||     raise Exception(msg) from e
              || Exception: Exception while calling the node's init() function.
              ||
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1365, in _do_call
              || |     return fn(*args)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1349, in _run_fn
              || |     return self._call_tf_sessionrun(options, feed_dict, fetch_list,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1441, in _call_tf_sessionrun
              || |     return tf_session.TF_SessionRun_wrapper(self._session, options, feed_dict,
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[{{node default_policy/functional_1_1/conv_value_1/Relu}}]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | During handling of the above exception, another exception occurred:
              || |
              || | Traceback (most recent call last):
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 86, in call
              || |     return session_or_none.run(symbolic_out[0], feed_dict)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 957, in run
              || |     result = self._run(None, fetches, feed_dict, options_ptr,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1180, in _run
              || |     results = self._do_run(handle, final_targets, final_fetches,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1358, in _do_run
              || |     return self._do_call(_run_fn, feeds, fetches, targets, options,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/client/session.py", line 1384, in _do_call
              || |     raise type(e)(node_def, op, message)
              || | tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
              || |   (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || |   (1) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
              || | 	 [[node default_policy/functional_1_1/conv_value_1/Relu (defined at /usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py:103) ]]
              || | 	 [[default_policy/strided_slice_1/_3]]
              || | 0 successful operations.
              || | 0 derived errors ignored.
              || |
              || | Original stack trace for 'default_policy/functional_1_1/conv_value_1/Relu':
              || |   File "solution.py", line 127, in <module>
              || |     main()
              || |   File "solution.py", line 123, in main
              || |     wrap_direct(node=node, protocol=protocol)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/interface.py", line 24, in wrap_direct
              || |     run_loop(node, protocol, args)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 243, in run_loop
              || |     loop(node_name, fi, fo, node, protocol, tin, tout, config=config, fi_desc=fin, fo_desc=fout)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              || |     call_if_fun_exists(node, "init", context=context_data)
              || |   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              || |     f(**kwargs)
              || |   File "solution.py", line 29, in init
              || |     self.model = RLlibModel(SEED,experiment_idx=0,checkpoint_idx=0,logger=context)
              || |   File "/submission/model.py", line 55, in __init__
              || |     self.model = PPOTrainer(config=config["rllib_config"])
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 90, in __init__
              || |     Trainer.__init__(self, config, env, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 455, in __init__
              || |     super().__init__(config, logger_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/tune/trainable.py", line 174, in __init__
              || |     self._setup(copy.deepcopy(self.config))
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 596, in _setup
              || |     self._init(self.config, self.env_creator)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer_template.py", line 115, in _init
              || |     self.workers = self._make_workers(env_creator, self._policy,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/trainer.py", line 662, in _make_workers
              || |     return WorkerSet(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 61, in __init__
              || |     self._local_worker = self._make_worker(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/worker_set.py", line 237, in _make_worker
              || |     worker = cls(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 360, in __init__
              || |     self._build_policy_map(policy_dict, policy_config)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/evaluation/rollout_worker.py", line 842, in _build_policy_map
              || |     policy_map[name] = cls(obs_space, act_space, merged_conf)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 129, in __init__
              || |     DynamicTFPolicy.__init__(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 237, in __init__
              || |     self._initialize_loss()
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/dynamic_tf_policy.py", line 324, in _initialize_loss
              || |     postprocessed_batch = self.postprocess_trajectory(
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/policy/tf_policy_template.py", line 155, in postprocess_trajectory
              || |     return postprocess_fn(self, sample_batch, other_agent_batches,
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 182, in postprocess_ppo_gae
              || |     last_r = policy._value(sample_batch[SampleBatch.NEXT_OBS][-1],
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/utils/tf_ops.py", line 84, in call
              || |     symbolic_out[0] = fn(*placeholders)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/agents/ppo/ppo_tf_policy.py", line 235, in value
              || |     model_out, _ = self.model({
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/modelv2.py", line 150, in __call__
              || |     res = self.forward(restored, state or [], seq_lens)
              || |   File "/usr/local/lib/python3.8/dist-packages/ray/rllib/models/tf/visionnet_v2.py", line 103, in forward
              || |     model_out, self._value_out = self.base_model(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 385, in call
              || |     return self._run_internal_graph(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph
              || |     outputs = node.layer(*args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/engine/base_layer_v1.py", line 776, in __call__
              || |     outputs = call_fn(cast_inputs, *args, **kwargs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/keras/layers/convolutional.py", line 269, in call
              || |     return self.activation(outputs)
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 10435, in relu
              || |     _, _, _op, _outputs = _op_def_library._apply_op_helper(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/op_def_library.py", line 742, in _apply_op_helper
              || |     op = g._create_op_internal(op_type_name, inputs, dtypes=None,
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 3477, in _create_op_internal
              || |     ret = Operation(
              || |   File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 1949, in __init__
              || |     self._traceback = tf_stack.extract_stack()
              || |
              || |
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 249, in main
    raise InvalidSubmission(msg) from e
duckietown_challenges.exceptions.InvalidSubmission: Getting agent protocol
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6211813556András Kalapos 🇭🇺real-v0.9-3092-363aido-LFP-sim-validation350successnoreg020:07:43
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survival_time_median5.324999999999989
in-drivable-lane_median0.0
driven_lanedir_consec_median1.9304158564653051
deviation-center-line_median0.2355752792466956


other stats
agent_compute-ego0_max0.04225463578195283
agent_compute-ego0_mean0.03684203977303549
agent_compute-ego0_median0.0370086540310406
agent_compute-ego0_min0.03109621524810791
complete-iteration_max0.517383686219803
complete-iteration_mean0.4580368870492344
complete-iteration_median0.4770187891351766
complete-iteration_min0.3607262837067815
deviation-center-line_max0.305290021708849
deviation-center-line_mean0.24418797361129485
deviation-center-line_min0.2003113142429392
deviation-heading_max0.8575584224474907
deviation-heading_mean0.69729156439083
deviation-heading_median0.7205211726396921
deviation-heading_min0.4905654898364451
driven_any_max2.4729798089162323
driven_any_mean1.7407682326336364
driven_any_median1.9493202435208667
driven_any_min0.5914526345765804
driven_lanedir_consec_max2.454537823955258
driven_lanedir_consec_mean1.6775960174849835
driven_lanedir_consec_min0.395014533054066
driven_lanedir_max2.454537823955258
driven_lanedir_mean1.6775960174849835
driven_lanedir_median1.9304158564653051
driven_lanedir_min0.395014533054066
get_duckie_state_max0.0951138714264179
get_duckie_state_mean0.06400737100948665
get_duckie_state_median0.07337351035590124
get_duckie_state_min0.014168591899726229
get_robot_state_max0.01529860702054254
get_robot_state_mean0.013785341295493192
get_robot_state_median0.014831216326656278
get_robot_state_min0.010180325508117675
get_state_dump_max0.031137901408071735
get_state_dump_mean0.028023325487432513
get_state_dump_median0.028027725257096243
get_state_dump_min0.024899950027465825
get_ui_image_max0.06504598771682893
get_ui_image_mean0.05827287086092431
get_ui_image_median0.05956504657350738
get_ui_image_min0.04891540257985355
in-drivable-lane_max0.40000000000000024
in-drivable-lane_mean0.10000000000000006
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.1554502635854904, "get_ui_image": 0.0544382539288751, "step_physics": 0.18710552412888096, "survival_time": 5.749999999999988, "driven_lanedir": 2.136280444030911, "get_state_dump": 0.0287810152974622, "get_robot_state": 0.01529860702054254, "sim_render-ego0": 0.011683213299718396, "get_duckie_state": 0.0951138714264179, "in-drivable-lane": 0.0, "deviation-heading": 0.7490430500128196, "agent_compute-ego0": 0.03756968111827456, "complete-iteration": 0.4683130671238077, "set_robot_commands": 0.008381074872510186, "deviation-center-line": 0.24107541612694527, "driven_lanedir_consec": 2.136280444030911, "sim_compute_sim_state": 0.021642742485835635, "sim_compute_performance-ego0": 0.008071800758098734}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.5914526345765804, "get_ui_image": 0.06469183921813965, "step_physics": 0.23394334316253665, "survival_time": 2.4499999999999993, "driven_lanedir": 0.395014533054066, "get_state_dump": 0.024899950027465825, "get_robot_state": 0.010180325508117675, "sim_render-ego0": 0.007971611022949219, "get_duckie_state": 0.0715959119796753, "in-drivable-lane": 0.40000000000000024, "deviation-heading": 0.4905654898364451, "agent_compute-ego0": 0.03109621524810791, "complete-iteration": 0.4857245111465454, "set_robot_commands": 0.006753568649291992, "deviation-center-line": 0.23007514236644588, "driven_lanedir_consec": 0.395014533054066, "sim_compute_sim_state": 0.0284035062789917, "sim_compute_performance-ego0": 0.005969390869140625}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.7431902234562429, "get_ui_image": 0.06504598771682893, "step_physics": 0.23237814084447997, "survival_time": 4.899999999999991, "driven_lanedir": 1.7245512688996991, "get_state_dump": 0.02727443521673029, "get_robot_state": 0.015264101702757556, "sim_render-ego0": 0.011373105675283103, "get_duckie_state": 0.07515110873212719, "in-drivable-lane": 0.0, "deviation-heading": 0.6919992952665646, "agent_compute-ego0": 0.04225463578195283, "complete-iteration": 0.517383686219803, "set_robot_commands": 0.008816162745157877, "deviation-center-line": 0.2003113142429392, "driven_lanedir_consec": 1.7245512688996991, "sim_compute_sim_state": 0.030932662462947343, "sim_compute_performance-ego0": 0.008675250140103426}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.4729798089162323, "get_ui_image": 0.04891540257985355, "step_physics": 0.1691152430672682, "survival_time": 6.499999999999985, "driven_lanedir": 2.454537823955258, "get_state_dump": 0.031137901408071735, "get_robot_state": 0.014398330950555, "sim_render-ego0": 0.009763479232788086, "get_duckie_state": 0.014168591899726229, "in-drivable-lane": 0.0, "deviation-heading": 0.8575584224474907, "agent_compute-ego0": 0.036447626943806655, "complete-iteration": 0.3607262837067815, "set_robot_commands": 0.007558959131022446, "deviation-center-line": 0.305290021708849, "driven_lanedir_consec": 2.454537823955258, "sim_compute_sim_state": 0.021652703976813164, "sim_compute_performance-ego0": 0.0073542449310535695}}
set_robot_commands_max0.008816162745157877
set_robot_commands_mean0.007877441349495626
set_robot_commands_median0.007970017001766316
set_robot_commands_min0.006753568649291992
sim_compute_performance-ego0_max0.008675250140103426
sim_compute_performance-ego0_mean0.007517671674599088
sim_compute_performance-ego0_median0.007713022844576152
sim_compute_performance-ego0_min0.005969390869140625
sim_compute_sim_state_max0.030932662462947343
sim_compute_sim_state_mean0.02565790380114696
sim_compute_sim_state_median0.025028105127902434
sim_compute_sim_state_min0.021642742485835635
sim_render-ego0_max0.011683213299718396
sim_render-ego0_mean0.0101978523076847
sim_render-ego0_median0.010568292454035596
sim_render-ego0_min0.007971611022949219
simulation-passed1
step_physics_max0.23394334316253665
step_physics_mean0.20563556280079143
step_physics_median0.2097418324866805
step_physics_min0.1691152430672682
survival_time_max6.499999999999985
survival_time_mean4.899999999999991
survival_time_min2.4499999999999993
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6211613563András Kalapos 🇭🇺real-v0.9-3092-363aido-LFV_multi-sim-validation356errornoreg020:05:39
InvalidEvaluator: Tr [...]
InvalidEvaluator:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 297, in read_reply
    raise ExternalTimeout(msg) from None
zuper_nodes.structures.ExternalTimeout: Timeout of 120 violated while waiting for None.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 383, in main
    raise dc.InvalidEvaluator(msg) from e
duckietown_challenges.exceptions.InvalidEvaluator: Anomalous error while running episodes:
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6211113567Márton Tim 🇭🇺3626aido-LFI-full-sim-validation363successnoreg020:09:52
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survival_time_median11.300000000000036
in-drivable-lane_median1.0999999999999992
driven_lanedir_consec_median4.5326961371923264
deviation-center-line_median0.7098198987918333


other stats
agent_compute-ego0_max0.08518331465513809
agent_compute-ego0_mean0.08473557335502443
agent_compute-ego0_median0.08473557335502443
agent_compute-ego0_min0.08428783205491078
complete-iteration_max0.4976913307024085
complete-iteration_mean0.4900816364247653
complete-iteration_median0.4900816364247653
complete-iteration_min0.48247194214712213
deviation-center-line_max1.0298821173642438
deviation-center-line_mean0.7098198987918333
deviation-center-line_min0.3897576802194227
deviation-heading_max2.698653448697972
deviation-heading_mean2.183164455868696
deviation-heading_median2.183164455868696
deviation-heading_min1.6676754630394195
driven_any_max7.529905657396592
driven_any_mean5.18195977715977
driven_any_median5.18195977715977
driven_any_min2.83401389692295
driven_lanedir_consec_max6.862367401540247
driven_lanedir_consec_mean4.5326961371923264
driven_lanedir_consec_min2.203024872844405
driven_lanedir_max7.039718281345724
driven_lanedir_mean4.652482980507246
driven_lanedir_median4.652482980507246
driven_lanedir_min2.265247679668769
get_duckie_state_max2.882903135275539e-06
get_duckie_state_mean2.847775650164378e-06
get_duckie_state_median2.847775650164378e-06
get_duckie_state_min2.812648165053216e-06
get_robot_state_max0.012572846080683457
get_robot_state_mean0.011777528583008396
get_robot_state_median0.011777528583008396
get_robot_state_min0.01098221108533334
get_state_dump_max0.02096193078635395
get_state_dump_mean0.019296424828676145
get_state_dump_median0.019296424828676145
get_state_dump_min0.017630918870998335
get_ui_image_max0.06726255969724794
get_ui_image_mean0.06532915329915671
get_ui_image_median0.06532915329915671
get_ui_image_min0.06339574690106549
in-drivable-lane_max1.5499999999999945
in-drivable-lane_mean1.0999999999999992
in-drivable-lane_min0.6500000000000039
per-episodes
details{"LFI-full-4way-000-ego0": {"driven_any": 2.83401389692295, "get_ui_image": 0.06726255969724794, "step_physics": 0.2600953872653021, "survival_time": 6.849999999999984, "driven_lanedir": 2.265247679668769, "get_state_dump": 0.02096193078635395, "get_robot_state": 0.01098221108533334, "sim_render-ego0": 0.010830848113350246, "get_duckie_state": 2.812648165053216e-06, "in-drivable-lane": 1.5499999999999945, "deviation-heading": 1.6676754630394195, "agent_compute-ego0": 0.08518331465513809, "complete-iteration": 0.4976913307024085, "set_robot_commands": 0.008142903231192326, "deviation-center-line": 0.3897576802194227, "driven_lanedir_consec": 2.203024872844405, "sim_compute_sim_state": 0.027097435965054276, "sim_compute_performance-ego0": 0.00690544515416242}, "LFI-full-udem1-000-ego0": {"driven_any": 7.529905657396592, "get_ui_image": 0.06339574690106549, "step_physics": 0.25007617398153376, "survival_time": 15.750000000000089, "driven_lanedir": 7.039718281345724, "get_state_dump": 0.017630918870998335, "get_robot_state": 0.012572846080683457, "sim_render-ego0": 0.009793752356420589, "get_duckie_state": 2.882903135275539e-06, "in-drivable-lane": 0.6500000000000039, "deviation-heading": 2.698653448697972, "agent_compute-ego0": 0.08428783205491078, "complete-iteration": 0.48247194214712213, "set_robot_commands": 0.008273040946525863, "deviation-center-line": 1.0298821173642438, "driven_lanedir_consec": 6.862367401540247, "sim_compute_sim_state": 0.029171578491790383, "sim_compute_performance-ego0": 0.0070517629007749915}}
set_robot_commands_max0.008273040946525863
set_robot_commands_mean0.008207972088859095
set_robot_commands_median0.008207972088859095
set_robot_commands_min0.008142903231192326
sim_compute_performance-ego0_max0.0070517629007749915
sim_compute_performance-ego0_mean0.0069786040274687055
sim_compute_performance-ego0_median0.0069786040274687055
sim_compute_performance-ego0_min0.00690544515416242
sim_compute_sim_state_max0.029171578491790383
sim_compute_sim_state_mean0.028134507228422333
sim_compute_sim_state_median0.028134507228422333
sim_compute_sim_state_min0.027097435965054276
sim_render-ego0_max0.010830848113350246
sim_render-ego0_mean0.010312300234885417
sim_render-ego0_median0.010312300234885417
sim_render-ego0_min0.009793752356420589
simulation-passed1
step_physics_max0.2600953872653021
step_physics_mean0.2550857806234179
step_physics_median0.2550857806234179
step_physics_min0.25007617398153376
survival_time_max15.750000000000089
survival_time_mean11.300000000000036
survival_time_min6.849999999999984
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6210813578Márton Tim 🇭🇺3626aido-LFV_multi-sim-validation356host-errornoreg020:01:48
InvalidEnvironment: [...]
InvalidEnvironment:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego1" aborted with the following error:

error in ego1 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(0, experiment_idx=0, checkpoint_idx=0, logger=context)
              ||   File "/submission/model.py", line 42, in __init__
              ||     dummy_env = wrap_env(config["env_config"], extra_config={
              ||   File "/submission/duckietown_utils/env.py", line 46, in wrap_env
              ||     env = SegmentObsWrapper(env, model=extra_config['model'])
              ||   File "/submission/duckietown_utils/wrappers/SegmentObsWrapper.py", line 43, in __init__
              ||     self.model.cuda()
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in cuda
              ||     return self._apply(lambda t: t.cuda(device))
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 381, in _apply
              ||     param_applied = fn(param)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in <lambda>
              ||     return self._apply(lambda t: t.cuda(device))
              || RuntimeError: CUDA error: out of memory
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 248, in main
    raise InvalidEnvironment(msg) from e
duckietown_challenges.exceptions.InvalidEnvironment: Detected out of CUDA memory:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego1" aborted with the following error:

error in ego1 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "solution.py", line 29, in init
              ||     self.model = RLlibModel(0, experiment_idx=0, checkpoint_idx=0, logger=context)
              ||   File "/submission/model.py", line 42, in __init__
              ||     dummy_env = wrap_env(config["env_config"], extra_config={
              ||   File "/submission/duckietown_utils/env.py", line 46, in wrap_env
              ||     env = SegmentObsWrapper(env, model=extra_config['model'])
              ||   File "/submission/duckietown_utils/wrappers/SegmentObsWrapper.py", line 43, in __init__
              ||     self.model.cuda()
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in cuda
              ||     return self._apply(lambda t: t.cuda(device))
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 381, in _apply
              ||     param_applied = fn(param)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in <lambda>
              ||     return self._apply(lambda t: t.cuda(device))
              || RuntimeError: CUDA error: out of memory
              ||
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6209613579Andras Beres202-1aido-LF-sim-testing348successnoreg021:12:00
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driven_lanedir_consec_median28.54292211860801
survival_time_median59.99999999999873
deviation-center-line_median4.063715948204074
in-drivable-lane_median1.4749999999999628


other stats
agent_compute-ego0_max0.1656152354390496
agent_compute-ego0_mean0.142004663749698
agent_compute-ego0_median0.1364188324303353
agent_compute-ego0_min0.12956575469907178
complete-iteration_max0.6554403745760826
complete-iteration_mean0.5415467553889126
complete-iteration_median0.5280729631500181
complete-iteration_min0.45460072067953167
deviation-center-line_max4.198790176270187
deviation-center-line_mean4.0097935112631085
deviation-center-line_min3.7129519723740994
deviation-heading_max10.501109418167191
deviation-heading_mean9.169146155376255
deviation-heading_median9.353276291646251
deviation-heading_min7.468922620045325
driven_any_max30.929537302909985
driven_any_mean29.456264031867512
driven_any_median29.67173905879683
driven_any_min27.5520407069664
driven_lanedir_consec_max30.346691393765976
driven_lanedir_consec_mean28.35996491236484
driven_lanedir_consec_min26.007324018477345
driven_lanedir_max30.346691393765976
driven_lanedir_mean28.35996491236484
driven_lanedir_median28.54292211860801
driven_lanedir_min26.007324018477345
get_duckie_state_max2.9158135635668195e-06
get_duckie_state_mean2.7967531615550275e-06
get_duckie_state_median2.762955690204452e-06
get_duckie_state_min2.7452877022443862e-06
get_robot_state_max0.01933438692561395
get_robot_state_mean0.014915271216288497
get_robot_state_median0.013820560151194651
get_robot_state_min0.012685577637150725
get_state_dump_max0.045169447383515346
get_state_dump_mean0.029085438366635057
get_state_dump_median0.02583079463139263
get_state_dump_min0.019510716820239624
get_ui_image_max0.07596327065428925
get_ui_image_mean0.06186375902852448
get_ui_image_median0.05924564555324584
get_ui_image_min0.05300047435331702
in-drivable-lane_max3.1499999999999275
in-drivable-lane_mean1.5249999999999633
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 30.929537302909985, "get_ui_image": 0.05300047435331702, "step_physics": 0.19137287477371, "survival_time": 59.99999999999873, "driven_lanedir": 30.346691393765976, "get_state_dump": 0.019510716820239624, "get_robot_state": 0.012685577637150725, "sim_render-ego0": 0.010426386508417566, "get_duckie_state": 2.7452877022443862e-06, "in-drivable-lane": 0.4999999999999858, "deviation-heading": 7.468922620045325, "agent_compute-ego0": 0.12956575469907178, "complete-iteration": 0.45460072067953167, "set_robot_commands": 0.006623462872342404, "deviation-center-line": 3.7129519723740994, "driven_lanedir_consec": 30.346691393765976, "sim_compute_sim_state": 0.024626432310830147, "sim_compute_performance-ego0": 0.00660694667838396}, "LF-norm-zigzag-000-ego0": {"driven_any": 27.5520407069664, "get_ui_image": 0.06397351853357168, "step_physics": 0.29530906319916, "survival_time": 59.99999999999873, "driven_lanedir": 26.007324018477345, "get_state_dump": 0.021756601571838223, "get_robot_state": 0.013083012673777409, "sim_render-ego0": 0.010741651703376357, "get_duckie_state": 2.9158135635668195e-06, "in-drivable-lane": 2.44999999999994, "deviation-heading": 10.501109418167191, "agent_compute-ego0": 0.13059077393899451, "complete-iteration": 0.5846112425579417, "set_robot_commands": 0.006791935078210378, "deviation-center-line": 3.9997216720454754, "driven_lanedir_consec": 26.007324018477345, "sim_compute_sim_state": 0.034658348431297385, "sim_compute_performance-ego0": 0.00752272296209915}, "LF-norm-techtrack-000-ego0": {"driven_any": 29.31637888848468, "get_ui_image": 0.07596327065428925, "step_physics": 0.269666473236211, "survival_time": 59.99999999999873, "driven_lanedir": 27.65188092892861, "get_state_dump": 0.045169447383515346, "get_robot_state": 0.01933438692561395, "sim_render-ego0": 0.014008129169105193, "get_duckie_state": 2.746081769118996e-06, "in-drivable-lane": 3.1499999999999275, "deviation-heading": 8.836119177628508, "agent_compute-ego0": 0.1656152354390496, "complete-iteration": 0.6554403745760826, "set_robot_commands": 0.010227077708057718, "deviation-center-line": 4.198790176270187, "driven_lanedir_consec": 27.65188092892861, "sim_compute_sim_state": 0.046432628321905714, "sim_compute_performance-ego0": 0.008840476940513948}, "LF-norm-small_loop-000-ego0": {"driven_any": 30.02709922910898, "get_ui_image": 0.05451777257291999, "step_physics": 0.182063338361513, "survival_time": 59.99999999999873, "driven_lanedir": 29.43396330828741, "get_state_dump": 0.029904987690947037, "get_robot_state": 0.0145581076286119, "sim_render-ego0": 0.011393344769569162, "get_duckie_state": 2.779829611289908e-06, "in-drivable-lane": 0.0, "deviation-heading": 9.870433405663997, "agent_compute-ego0": 0.14224689092167608, "complete-iteration": 0.4715346837420944, "set_robot_commands": 0.009604200534677624, "deviation-center-line": 4.127710224362671, "driven_lanedir_consec": 29.43396330828741, "sim_compute_sim_state": 0.01965230350986706, "sim_compute_performance-ego0": 0.00741643155246452}}
set_robot_commands_max0.010227077708057718
set_robot_commands_mean0.008311669048322031
set_robot_commands_median0.008198067806444002
set_robot_commands_min0.006623462872342404
sim_compute_performance-ego0_max0.008840476940513948
sim_compute_performance-ego0_mean0.007596644533365394
sim_compute_performance-ego0_median0.007469577257281835
sim_compute_performance-ego0_min0.00660694667838396
sim_compute_sim_state_max0.046432628321905714
sim_compute_sim_state_mean0.03134242814347508
sim_compute_sim_state_median0.029642390371063768
sim_compute_sim_state_min0.01965230350986706
sim_render-ego0_max0.014008129169105193
sim_render-ego0_mean0.01164237803761707
sim_render-ego0_median0.01106749823647276
sim_render-ego0_min0.010426386508417566
simulation-passed1
step_physics_max0.29530906319916
step_physics_mean0.2346029373926485
step_physics_median0.2305196740049605
step_physics_min0.182063338361513
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
No reset possible
6209413586Andras Beres202-1aido-LFP-sim-validation350successnoreg020:08:14
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survival_time_median4.6499999999999915
in-drivable-lane_median0.0
driven_lanedir_consec_median1.7964620401090152
deviation-center-line_median0.2765538903866969


other stats
agent_compute-ego0_max0.15565981409128973
agent_compute-ego0_mean0.14086457608675698
agent_compute-ego0_median0.14519330344177864
agent_compute-ego0_min0.11741188337218086
complete-iteration_max0.6552236384533822
complete-iteration_mean0.5995997089074432
complete-iteration_median0.5991312076954374
complete-iteration_min0.5449127817855162
deviation-center-line_max0.4257199605729894
deviation-center-line_mean0.2926605254610903
deviation-center-line_min0.19181436049797795
deviation-heading_max1.214770654593495
deviation-heading_mean0.7819174152775236
deviation-heading_median0.7646811555370905
deviation-heading_min0.38353669544241825
driven_any_max2.646667534116804
driven_any_mean1.7092015228716133
driven_any_median1.82462434016342
driven_any_min0.540889877042809
driven_lanedir_consec_max2.593616185382574
driven_lanedir_consec_mean1.680196324786399
driven_lanedir_consec_min0.53424503354499
driven_lanedir_max2.593616185382574
driven_lanedir_mean1.680196324786399
driven_lanedir_median1.7964620401090152
driven_lanedir_min0.53424503354499
get_duckie_state_max0.09259184351507224
get_duckie_state_mean0.06973125714938111
get_duckie_state_median0.08233392606699597
get_duckie_state_min0.0216653329484603
get_robot_state_max0.015259073135700633
get_robot_state_mean0.013823814685568637
get_robot_state_median0.013828391769485388
get_robot_state_min0.012379402067603134
get_state_dump_max0.03910129628282912
get_state_dump_mean0.0364045839134846
get_state_dump_median0.03705152678588432
get_state_dump_min0.03241398579934064
get_ui_image_max0.079325980328499
get_ui_image_mean0.06685979811312115
get_ui_image_median0.06455635776478727
get_ui_image_min0.05900049659441102
in-drivable-lane_max0.0
in-drivable-lane_mean0.0
in-drivable-lane_min0.0
per-episodes
details{"LFP-norm-loop-000-ego0": {"driven_any": 2.18916975561165, "get_ui_image": 0.05900049659441102, "step_physics": 0.19102853873990616, "survival_time": 5.249999999999989, "driven_lanedir": 2.1640796525441077, "get_state_dump": 0.035444635265278364, "get_robot_state": 0.012620228641438034, "sim_render-ego0": 0.009595789999332067, "get_duckie_state": 0.09259184351507224, "in-drivable-lane": 0.0, "deviation-heading": 0.750563343355278, "agent_compute-ego0": 0.11741188337218086, "complete-iteration": 0.5660520589576578, "set_robot_commands": 0.007176048350784014, "deviation-center-line": 0.2757789780075416, "driven_lanedir_consec": 2.1640796525441077, "sim_compute_sim_state": 0.031230935510599387, "sim_compute_performance-ego0": 0.009736425471755692}, "LFP-norm-zigzag-000-ego0": {"driven_any": 0.540889877042809, "get_ui_image": 0.079325980328499, "step_physics": 0.25082636386790175, "survival_time": 2.3, "driven_lanedir": 0.53424503354499, "get_state_dump": 0.03910129628282912, "get_robot_state": 0.015259073135700633, "sim_render-ego0": 0.008514713733754259, "get_duckie_state": 0.07390686806212081, "in-drivable-lane": 0.0, "deviation-heading": 0.38353669544241825, "agent_compute-ego0": 0.1475325036556163, "complete-iteration": 0.6552236384533822, "set_robot_commands": 0.005893179710875166, "deviation-center-line": 0.19181436049797795, "driven_lanedir_consec": 0.53424503354499, "sim_compute_sim_state": 0.02273569715783951, "sim_compute_performance-ego0": 0.01194270113681225}, "LFP-norm-techtrack-000-ego0": {"driven_any": 1.4600789247151902, "get_ui_image": 0.0669092521434877, "step_physics": 0.22308869478179189, "survival_time": 4.049999999999994, "driven_lanedir": 1.428844427673923, "get_state_dump": 0.03865841830649027, "get_robot_state": 0.012379402067603134, "sim_render-ego0": 0.010101021789922946, "get_duckie_state": 0.09076098407187112, "in-drivable-lane": 0.0, "deviation-heading": 0.778798967718903, "agent_compute-ego0": 0.142854103227941, "complete-iteration": 0.6322103564332171, "set_robot_commands": 0.008208094573602444, "deviation-center-line": 0.27732880276585226, "driven_lanedir_consec": 1.428844427673923, "sim_compute_sim_state": 0.034232750171568335, "sim_compute_performance-ego0": 0.004819474569181117}, "LFP-norm-small_loop-000-ego0": {"driven_any": 2.646667534116804, "get_ui_image": 0.06220346338608686, "step_physics": 0.20527100387741537, "survival_time": 6.749999999999984, "driven_lanedir": 2.593616185382574, "get_state_dump": 0.03241398579934064, "get_robot_state": 0.015036554897532743, "sim_render-ego0": 0.01467286839204676, "get_duckie_state": 0.0216653329484603, "in-drivable-lane": 0.0, "deviation-heading": 1.214770654593495, "agent_compute-ego0": 0.15565981409128973, "complete-iteration": 0.5449127817855162, "set_robot_commands": 0.009772330522537231, "deviation-center-line": 0.4257199605729894, "driven_lanedir_consec": 2.593616185382574, "sim_compute_sim_state": 0.021326866220025456, "sim_compute_performance-ego0": 0.006708371288636152}}
set_robot_commands_max0.009772330522537231
set_robot_commands_mean0.0077624132894497135
set_robot_commands_median0.007692071462193229
set_robot_commands_min0.005893179710875166
sim_compute_performance-ego0_max0.01194270113681225
sim_compute_performance-ego0_mean0.008301743116596304
sim_compute_performance-ego0_median0.008222398380195922
sim_compute_performance-ego0_min0.004819474569181117
sim_compute_sim_state_max0.034232750171568335
sim_compute_sim_state_mean0.02738156226500817
sim_compute_sim_state_median0.02698331633421945
sim_compute_sim_state_min0.021326866220025456
sim_render-ego0_max0.01467286839204676
sim_render-ego0_mean0.01072109847876401
sim_render-ego0_median0.009848405894627509
sim_render-ego0_min0.008514713733754259
simulation-passed1
step_physics_max0.25082636386790175
step_physics_mean0.2175536503167538
step_physics_median0.21417984932960363
step_physics_min0.19102853873990616
survival_time_max6.749999999999984
survival_time_mean4.5874999999999915
survival_time_min2.3
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6207513609Andras Beresfsf+ilaido-LFV_multi-sim-validation356successnoreg021:28:27
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survival_time_median25.500000000000227
in-drivable-lane_median0.1750000000000016
driven_lanedir_consec_median5.360290638140267
deviation-center-line_median1.6419512001605852


other stats
agent_compute-ego0_max0.1905884283669987
agent_compute-ego0_mean0.13749655981614667
agent_compute-ego0_median0.13020304877697372
agent_compute-ego0_min0.12516921183030552
agent_compute-ego1_max0.199099684353941
agent_compute-ego1_mean0.12487586517596448
agent_compute-ego1_median0.11134529707959136
agent_compute-ego1_min0.10883233615820356
complete-iteration_max1.7643214470377098
complete-iteration_mean1.478999549848004
complete-iteration_median1.6127399824370143
complete-iteration_min1.077682435882758
deviation-center-line_max4.685258885331869
deviation-center-line_mean2.1099678146582512
deviation-center-line_min0.46580390692958307
deviation-heading_max14.88277701798551
deviation-heading_mean5.191134890418641
deviation-heading_median3.4508961524946553
deviation-heading_min1.1525089054371125
driven_any_max22.4584202936406
driven_any_mean7.736861551751716
driven_any_median5.422717717740274
driven_any_min1.0082410328396767
driven_lanedir_consec_max21.08988072347855
driven_lanedir_consec_mean7.364004911170505
driven_lanedir_consec_min0.9961498420037288
driven_lanedir_max21.08988072347855
driven_lanedir_mean7.364004911170505
driven_lanedir_median5.360290638140267
driven_lanedir_min0.9961498420037288
get_duckie_state_max3.5479772005149787e-06
get_duckie_state_mean3.275568963865196e-06
get_duckie_state_median3.3460302144938913e-06
get_duckie_state_min2.8077119625873447e-06
get_robot_state_max0.051953022428553736
get_robot_state_mean0.04931664195184721
get_robot_state_median0.04988546782441104
get_robot_state_min0.04426761443570534
get_state_dump_max0.07501767733082268
get_state_dump_mean0.0389291545641392
get_state_dump_median0.03282281369558871
get_state_dump_min0.031126749258918535
get_ui_image_max0.08891100378422723
get_ui_image_mean0.08305114146087998
get_ui_image_median0.08469485023483604
get_ui_image_min0.07662444085067843
in-drivable-lane_max7.0000000000000995
in-drivable-lane_mean1.07142857142858
in-drivable-lane_min0.0
per-episodes
details{"LFV_multi-norm-loop-000-ego0": {"driven_any": 1.0082410328396767, "get_ui_image": 0.07876092066867746, "step_physics": 0.4990105414562088, "survival_time": 13.850000000000062, "driven_lanedir": 0.9961498420037288, "get_state_dump": 0.03479363935456859, "get_robot_state": 0.051953022428553736, "sim_render-ego0": 0.010964995665515929, "sim_render-ego1": 0.008975956079771193, "sim_render-ego2": 0.00990116510459845, "sim_render-ego3": 0.010688417249446292, "get_duckie_state": 3.5479772005149787e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.545925571247038, "agent_compute-ego0": 0.13057148456573486, "agent_compute-ego1": 0.10883233615820356, "agent_compute-ego2": 0.11363598854421712, "agent_compute-ego3": 0.10833718021996588, "complete-iteration": 1.2605957770519118, "set_robot_commands": 0.006361283844323467, "deviation-center-line": 1.735239931417952, "driven_lanedir_consec": 0.9961498420037288, "sim_compute_sim_state": 0.038162384959433575, "sim_compute_performance-ego0": 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0.03282281369558871, "get_robot_state": 0.04988546782441104, "sim_render-ego0": 0.010189216332519783, "sim_render-ego1": 0.00966342067421411, "sim_render-ego2": 0.009309081149868751, "sim_render-ego3": 0.008682929095449477, "get_duckie_state": 3.3460302144938913e-06, "in-drivable-lane": 2.599999999999987, "deviation-heading": 7.488414182630852, "agent_compute-ego0": 0.12516921183030552, "agent_compute-ego1": 0.11134529707959136, "agent_compute-ego2": 0.1091438382090314, "agent_compute-ego3": 0.1093684874343476, "complete-iteration": 1.7643214470377098, "set_robot_commands": 0.007262798855980608, "deviation-center-line": 3.5117593562722984, "driven_lanedir_consec": 21.08988072347855, "sim_compute_sim_state": 0.06477536442123841, "sim_compute_performance-ego0": 0.007168443527300905, "sim_compute_performance-ego1": 0.005452958718019606, "sim_compute_performance-ego2": 0.005030844441826841, "sim_compute_performance-ego3": 0.005130230328251887}, "LFV_multi-norm-zigzag-000-ego1": 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0.007777303632224843, "sim_render-ego3": 0.009375058974771816, "get_duckie_state": 3.1666279772256454e-06, "in-drivable-lane": 7.0000000000000995, "deviation-heading": 3.355866733742272, "agent_compute-ego0": 0.13020304877697372, "agent_compute-ego1": 0.11733805270111024, "agent_compute-ego2": 0.11416787690612434, "agent_compute-ego3": 0.11179864523228834, "complete-iteration": 1.6127399824370143, "set_robot_commands": 0.007825631218180498, "deviation-center-line": 1.1572362815813495, "driven_lanedir_consec": 8.76615241756709, "sim_compute_sim_state": 0.05593560959728263, "sim_compute_performance-ego0": 0.007174836679447421, "sim_compute_performance-ego1": 0.005075638541503428, "sim_compute_performance-ego2": 0.00490181133471823, "sim_compute_performance-ego3": 0.005515518487083002}, "LFV_multi-norm-techtrack-000-ego1": {"driven_any": 3.634422435690906, "get_ui_image": 0.08469485023483604, "step_physics": 0.8311178642243089, "survival_time": 25.500000000000227, "driven_lanedir": 3.060634333986818, "get_state_dump": 0.031126749258918535, "get_robot_state": 0.04863594936064778, "sim_render-ego0": 0.010688935240654096, "sim_render-ego1": 0.009216306727459753, "sim_render-ego2": 0.007777303632224843, "sim_render-ego3": 0.009375058974771816, "get_duckie_state": 3.1666279772256454e-06, "in-drivable-lane": 1.3000000000000007, "deviation-heading": 9.103409844428269, "agent_compute-ego0": 0.13020304877697372, "agent_compute-ego1": 0.11733805270111024, "agent_compute-ego2": 0.11416787690612434, "agent_compute-ego3": 0.11179864523228834, "complete-iteration": 1.6127399824370143, "set_robot_commands": 0.007825631218180498, "deviation-center-line": 3.5437718854438516, "driven_lanedir_consec": 3.060634333986818, "sim_compute_sim_state": 0.05593560959728263, "sim_compute_performance-ego0": 0.007174836679447421, "sim_compute_performance-ego1": 0.005075638541503428, "sim_compute_performance-ego2": 0.00490181133471823, "sim_compute_performance-ego3": 0.005515518487083002}, "LFV_multi-norm-techtrack-000-ego2": {"driven_any": 4.131995111470532, "get_ui_image": 0.08469485023483604, "step_physics": 0.8311178642243089, "survival_time": 25.500000000000227, "driven_lanedir": 4.082488962295669, "get_state_dump": 0.031126749258918535, "get_robot_state": 0.04863594936064778, "sim_render-ego0": 0.010688935240654096, "sim_render-ego1": 0.009216306727459753, "sim_render-ego2": 0.007777303632224843, "sim_render-ego3": 0.009375058974771816, "get_duckie_state": 3.1666279772256454e-06, "in-drivable-lane": 0.0, "deviation-heading": 3.200506294096474, "agent_compute-ego0": 0.13020304877697372, "agent_compute-ego1": 0.11733805270111024, "agent_compute-ego2": 0.11416787690612434, "agent_compute-ego3": 0.11179864523228834, "complete-iteration": 1.6127399824370143, "set_robot_commands": 0.007825631218180498, "deviation-center-line": 2.793224769868812, "driven_lanedir_consec": 4.082488962295669, "sim_compute_sim_state": 0.05593560959728263, "sim_compute_performance-ego0": 0.007174836679447421, "sim_compute_performance-ego1": 0.005075638541503428, "sim_compute_performance-ego2": 0.00490181133471823, "sim_compute_performance-ego3": 0.005515518487083002}, "LFV_multi-norm-techtrack-000-ego3": {"driven_any": 12.166247135608948, "get_ui_image": 0.08469485023483604, "step_physics": 0.8311178642243089, "survival_time": 25.500000000000227, "driven_lanedir": 11.573623696993607, "get_state_dump": 0.031126749258918535, "get_robot_state": 0.04863594936064778, "sim_render-ego0": 0.010688935240654096, "sim_render-ego1": 0.009216306727459753, "sim_render-ego2": 0.007777303632224843, "sim_render-ego3": 0.009375058974771816, "get_duckie_state": 3.1666279772256454e-06, "in-drivable-lane": 1.050000000000015, "deviation-heading": 3.939493375876067, "agent_compute-ego0": 0.13020304877697372, "agent_compute-ego1": 0.11733805270111024, "agent_compute-ego2": 0.11416787690612434, "agent_compute-ego3": 0.11179864523228834, "complete-iteration": 1.6127399824370143, "set_robot_commands": 0.007825631218180498, "deviation-center-line": 1.548662468903218, "driven_lanedir_consec": 11.573623696993607, "sim_compute_sim_state": 0.05593560959728263, "sim_compute_performance-ego0": 0.007174836679447421, "sim_compute_performance-ego1": 0.005075638541503428, "sim_compute_performance-ego2": 0.00490181133471823, "sim_compute_performance-ego3": 0.005515518487083002}, "LFV_multi-norm-small_loop-000-ego0": {"driven_any": 3.542051695809606, "get_ui_image": 0.07662444085067843, "step_physics": 0.3555649245007438, "survival_time": 7.99999999999998, "driven_lanedir": 3.485589746288337, "get_state_dump": 0.07501767733082268, "get_robot_state": 0.04426761443570534, "sim_render-ego0": 0.01937699169846055, "sim_render-ego1": 0.014648483406682932, "get_duckie_state": 2.8077119625873447e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.1525089054371125, "agent_compute-ego0": 0.1905884283669987, "agent_compute-ego1": 0.199099684353941, "complete-iteration": 1.077682435882758, "set_robot_commands": 0.0135178047677745, "deviation-center-line": 0.5244579909543857, "driven_lanedir_consec": 3.485589746288337, "sim_compute_sim_state": 0.04736285476210695, "sim_compute_performance-ego0": 0.018840495103634666, "sim_compute_performance-ego1": 0.007579550239610376}, "LFV_multi-norm-small_loop-000-ego1": {"driven_any": 3.222519519891979, "get_ui_image": 0.07662444085067843, "step_physics": 0.3555649245007438, "survival_time": 7.99999999999998, "driven_lanedir": 3.1399072970072126, "get_state_dump": 0.07501767733082268, "get_robot_state": 0.04426761443570534, "sim_render-ego0": 0.01937699169846055, "sim_render-ego1": 0.014648483406682932, "get_duckie_state": 2.8077119625873447e-06, "in-drivable-lane": 0.0, "deviation-heading": 1.6314448400562316, "agent_compute-ego0": 0.1905884283669987, "agent_compute-ego1": 0.199099684353941, "complete-iteration": 1.077682435882758, "set_robot_commands": 0.0135178047677745, "deviation-center-line": 0.46580390692958307, "driven_lanedir_consec": 3.1399072970072126, "sim_compute_sim_state": 0.04736285476210695, "sim_compute_performance-ego0": 0.018840495103634666, "sim_compute_performance-ego1": 0.007579550239610376}}
set_robot_commands_max0.0135178047677745
set_robot_commands_mean0.008059604657820522
set_robot_commands_median0.007262798855980608
set_robot_commands_min0.006361283844323467
sim_compute_performance-ego0_max0.018840495103634666
sim_compute_performance-ego0_mean0.008655294913759925
sim_compute_performance-ego0_median0.007168443527300905
sim_compute_performance-ego0_min0.006530004439594076
sim_compute_performance-ego1_max0.007579550239610376
sim_compute_performance-ego1_mean0.005897586078404017
sim_compute_performance-ego1_median0.005452958718019606
sim_compute_performance-ego1_min0.005075638541503428
sim_compute_sim_state_max0.06477536442123841
sim_compute_sim_state_mean0.052158510388288025
sim_compute_sim_state_median0.05593560959728263
sim_compute_sim_state_min0.038162384959433575
sim_render-ego0_max0.01937699169846055
sim_render-ego0_mean0.011866183739405735
sim_render-ego0_median0.010688935240654096
sim_render-ego0_min0.010189216332519783
sim_render-ego1_max0.014648483406682932
sim_render-ego1_mean0.010051407195653292
sim_render-ego1_median0.009216306727459753
sim_render-ego1_min0.008975956079771193
simulation-passed1
step_physics_max0.9845537465928376
step_physics_mean0.7121327470067792
step_physics_median0.8311178642243089
step_physics_min0.3555649245007438
survival_time_max48.099999999999405
survival_time_mean26.12857142857134
survival_time_min7.99999999999998
No reset possible
6206713625Raphael Jeanmobile-segmentationaido-LF-sim-testing348successnoreg021:12:49
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driven_lanedir_consec_median27.152484990780785
survival_time_median59.99999999999873
deviation-center-line_median2.2313238678044396
in-drivable-lane_median0.0


other stats
agent_compute-ego0_max0.2052702334004576
agent_compute-ego0_mean0.1811578692147178
agent_compute-ego0_median0.20236379607928784
agent_compute-ego0_min0.11463365129983792
complete-iteration_max0.6489576687920005
complete-iteration_mean0.5664365357602268
complete-iteration_median0.5474560252832036
complete-iteration_min0.5218764236824995
deviation-center-line_max2.5527653314327057
deviation-center-line_mean2.1746102612383953
deviation-center-line_min1.683027977911996
deviation-heading_max10.539102670037256
deviation-heading_mean9.096134494986105
deviation-heading_median8.945611422888252
deviation-heading_min7.954212464130658
driven_any_max28.120266416729564
driven_any_mean27.57253139878999
driven_any_median27.579958215947485
driven_any_min27.009942746535422
driven_lanedir_consec_max27.686279566366085
driven_lanedir_consec_mean26.88534278850939
driven_lanedir_consec_min25.550121606109904
driven_lanedir_max27.686279566366085
driven_lanedir_mean26.88534278850939
driven_lanedir_median27.152484990780785
driven_lanedir_min25.550121606109904
get_duckie_state_max3.359498429754989e-06
get_duckie_state_mean3.1931910487039203e-06
get_duckie_state_median3.1764660151574533e-06
get_duckie_state_min3.060333734745785e-06
get_robot_state_max0.013028536509911684
get_robot_state_mean0.012485267319945271
get_robot_state_median0.012800714852510144
get_robot_state_min0.011311103064849117
get_state_dump_max0.021640183824385137
get_state_dump_mean0.01941509627978272
get_state_dump_median0.019070193332795995
get_state_dump_min0.017879814629153744
get_ui_image_max0.06248274552236489
get_ui_image_mean0.05565342567643952
get_ui_image_median0.055728465293865215
get_ui_image_min0.04867402659566277
in-drivable-lane_max1.9500000000000215
in-drivable-lane_mean0.4875000000000054
in-drivable-lane_min0.0
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 27.92982856232948, "get_ui_image": 0.052628394864580215, "step_physics": 0.22152153578130132, "survival_time": 59.99999999999873, "driven_lanedir": 27.570773795906938, "get_state_dump": 0.021640183824385137, "get_robot_state": 0.011311103064849117, "sim_render-ego0": 0.011443048393001762, "get_duckie_state": 3.149666258139376e-06, "in-drivable-lane": 0.0, "deviation-heading": 7.954212464130658, "agent_compute-ego0": 0.2052702334004576, "complete-iteration": 0.5642528222264298, "set_robot_commands": 0.008888255150292339, "deviation-center-line": 1.683027977911996, "driven_lanedir_consec": 27.570773795906938, "sim_compute_sim_state": 0.024537188722926512, "sim_compute_performance-ego0": 0.0068024757204206655}, "LF-norm-zigzag-000-ego0": {"driven_any": 27.23008786956549, "get_ui_image": 0.06248274552236489, "step_physics": 0.2939328464441355, "survival_time": 59.99999999999873, "driven_lanedir": 26.73419618565463, "get_state_dump": 0.01923157194075636, "get_robot_state": 0.012906518407308687, "sim_render-ego0": 0.010011467905862444, "get_duckie_state": 3.203265772175531e-06, "in-drivable-lane": 0.0, "deviation-heading": 9.34781715237032, "agent_compute-ego0": 0.19964798145945328, "complete-iteration": 0.6489576687920005, "set_robot_commands": 0.010584158663150175, "deviation-center-line": 2.5054815520835567, "driven_lanedir_consec": 26.73419618565463, "sim_compute_sim_state": 0.033170553766420544, "sim_compute_performance-ego0": 0.006776239155333406}, "LF-norm-techtrack-000-ego0": {"driven_any": 27.009942746535422, "get_ui_image": 0.05882853572315022, "step_physics": 0.2701308230972608, "survival_time": 59.99999999999873, "driven_lanedir": 25.550121606109904, "get_state_dump": 0.017879814629153744, "get_robot_state": 0.013028536509911684, "sim_render-ego0": 0.010373660070910043, "get_duckie_state": 3.060333734745785e-06, "in-drivable-lane": 1.9500000000000215, "deviation-heading": 10.539102670037256, "agent_compute-ego0": 0.11463365129983792, "complete-iteration": 0.5306592283399774, "set_robot_commands": 0.007895719399559409, "deviation-center-line": 2.5527653314327057, "driven_lanedir_consec": 25.550121606109904, "sim_compute_sim_state": 0.030693351974296727, "sim_compute_performance-ego0": 0.006986702808631052}, "LF-norm-small_loop-000-ego0": {"driven_any": 28.120266416729564, "get_ui_image": 0.04867402659566277, "step_physics": 0.19400548815826493, "survival_time": 59.99999999999873, "driven_lanedir": 27.686279566366085, "get_state_dump": 0.01890881472483563, "get_robot_state": 0.012694911297711603, "sim_render-ego0": 0.010058971765535656, "get_duckie_state": 3.359498429754989e-06, "in-drivable-lane": 0.0, "deviation-heading": 8.543405693406182, "agent_compute-ego0": 0.2050796106991224, "complete-iteration": 0.5218764236824995, "set_robot_commands": 0.008809526397425566, "deviation-center-line": 1.957166183525322, "driven_lanedir_consec": 27.686279566366085, "sim_compute_sim_state": 0.01680278778076172, "sim_compute_performance-ego0": 0.006631545877575775}}
set_robot_commands_max0.010584158663150175
set_robot_commands_mean0.009044414902606872
set_robot_commands_median0.008848890773858952
set_robot_commands_min0.007895719399559409
sim_compute_performance-ego0_max0.006986702808631052
sim_compute_performance-ego0_mean0.006799240890490225
sim_compute_performance-ego0_median0.006789357437877036
sim_compute_performance-ego0_min0.006631545877575775
sim_compute_sim_state_max0.033170553766420544
sim_compute_sim_state_mean0.026300970561101377
sim_compute_sim_state_median0.02761527034861162
sim_compute_sim_state_min0.01680278778076172
sim_render-ego0_max0.011443048393001762
sim_render-ego0_mean0.010471787033827475
sim_render-ego0_median0.010216315918222852
sim_render-ego0_min0.010011467905862444
simulation-passed1
step_physics_max0.2939328464441355
step_physics_mean0.24489767337024065
step_physics_median0.24582617943928103
step_physics_min0.19400548815826493
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
No reset possible
6206513626Raphael Jeanmobile-segmentationaido-LF-sim-validation347failednoreg020:03:50
InvalidSubmission: T [...]
InvalidSubmission:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 190, in main
    raise InvalidSubmission(msg)
duckietown_challenges.exceptions.InvalidSubmission: Timeout during connection to ego0: <SignalTimeout in state: 2>
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No reset possible
6205813640Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LF-sim-testing348successnoreg021:13:34
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driven_lanedir_consec_median25.852702117456506
survival_time_median59.99999999999873
deviation-center-line_median3.9556165277028423
in-drivable-lane_median1.1999999999999758


other stats
agent_compute-ego0_max0.23730366216114815
agent_compute-ego0_mean0.19896065970443863
agent_compute-ego0_median0.2314601497388105
agent_compute-ego0_min0.09561867717898558
complete-iteration_max0.683411168813904
complete-iteration_mean0.5786985945046494
complete-iteration_median0.5667652716545339
complete-iteration_min0.4978526658956256
deviation-center-line_max4.278006103752316
deviation-center-line_mean3.801250272125431
deviation-center-line_min3.015761929343726
deviation-heading_max14.808898485441834
deviation-heading_mean11.029258448053278
deviation-heading_median10.310393137429571
deviation-heading_min8.687349031912142
driven_any_max27.541554960162912
driven_any_mean27.14400569805142
driven_any_median27.174708279408513
driven_any_min26.685051273225746
driven_lanedir_consec_max26.50060926305035
driven_lanedir_consec_mean25.751615124344436
driven_lanedir_consec_min24.80044699941439
driven_lanedir_max26.50060926305035
driven_lanedir_mean25.751615124344436
driven_lanedir_median25.852702117456506
driven_lanedir_min24.80044699941439
get_duckie_state_max3.2110079242029756e-06
get_duckie_state_mean3.1011289204288584e-06
get_duckie_state_median3.0948756437913067e-06
get_duckie_state_min3.003756469929843e-06
get_robot_state_max0.01341493719324879
get_robot_state_mean0.01271048731649051
get_robot_state_median0.012628699420989304
get_robot_state_min0.012169613230734642
get_state_dump_max0.021695316483039444
get_state_dump_mean0.01943649945906259
get_state_dump_median0.01981974223769773
get_state_dump_min0.016411196877815445
get_ui_image_max0.06620443452903373
get_ui_image_mean0.056820170815838664
get_ui_image_median0.055125994051028845
get_ui_image_min0.05082426063226324
in-drivable-lane_max1.7499999999999574
in-drivable-lane_mean1.2499999999999785
in-drivable-lane_min0.850000000000005
per-episodes
details{"LF-norm-loop-000-ego0": {"driven_any": 27.541554960162912, "get_ui_image": 0.05324929064259144, "step_physics": 0.20569454581413937, "survival_time": 59.99999999999873, "driven_lanedir": 26.50060926305035, "get_state_dump": 0.021695316483039444, "get_robot_state": 0.01341493719324879, "sim_render-ego0": 0.011045359056458485, "get_duckie_state": 3.0821705737975516e-06, "in-drivable-lane": 1.1499999999999757, "deviation-heading": 8.687349031912142, "agent_compute-ego0": 0.22681671276775428, "complete-iteration": 0.5730550485685604, "set_robot_commands": 0.009512071506268377, "deviation-center-line": 3.6577843314723686, "driven_lanedir_consec": 26.50060926305035, "sim_compute_sim_state": 0.02423981504575299, "sim_compute_performance-ego0": 0.007175738765834075}, "LF-norm-zigzag-000-ego0": {"driven_any": 26.99868906424442, "get_ui_image": 0.06620443452903373, "step_physics": 0.2845895068036031, "survival_time": 59.99999999999873, "driven_lanedir": 24.80044699941439, "get_state_dump": 0.019724662059749, "get_robot_state": 0.0125955721420809, "sim_render-ego0": 0.01113991038586873, "get_duckie_state": 3.2110079242029756e-06, "in-drivable-lane": 1.7499999999999574, "deviation-heading": 14.808898485441834, "agent_compute-ego0": 0.23730366216114815, "complete-iteration": 0.683411168813904, "set_robot_commands": 0.008787414414201748, "deviation-center-line": 4.278006103752316, "driven_lanedir_consec": 24.80044699941439, "sim_compute_sim_state": 0.0352780584689481, "sim_compute_performance-ego0": 0.007594122874746712}, "LF-norm-techtrack-000-ego0": {"driven_any": 26.685051273225746, "get_ui_image": 0.057002697459466255, "step_physics": 0.26285689915348154, "survival_time": 59.99999999999873, "driven_lanedir": 25.504815888226982, "get_state_dump": 0.016411196877815445, "get_robot_state": 0.012169613230734642, "sim_render-ego0": 0.010012043802863257, "get_duckie_state": 3.107580713785062e-06, "in-drivable-lane": 0.850000000000005, "deviation-heading": 11.179958174583897, "agent_compute-ego0": 0.09561867717898558, "complete-iteration": 0.4978526658956256, "set_robot_commands": 0.007677214628056026, "deviation-center-line": 3.015761929343726, "driven_lanedir_consec": 25.504815888226982, "sim_compute_sim_state": 0.0293548726519379, "sim_compute_performance-ego0": 0.0065576180530328935}, "LF-norm-small_loop-000-ego0": {"driven_any": 27.3507274945726, "get_ui_image": 0.05082426063226324, "step_physics": 0.19625355480711823, "survival_time": 59.99999999999873, "driven_lanedir": 26.20058834668602, "get_state_dump": 0.019914822415646467, "get_robot_state": 0.012661826699897709, "sim_render-ego0": 0.010408768943704036, "get_duckie_state": 3.003756469929843e-06, "in-drivable-lane": 1.249999999999976, "deviation-heading": 9.440828100275246, "agent_compute-ego0": 0.2361035867098666, "complete-iteration": 0.5604754947405076, "set_robot_commands": 0.008664682842511916, "deviation-center-line": 4.2534487239333165, "driven_lanedir_consec": 26.20058834668602, "sim_compute_sim_state": 0.01843017166004292, "sim_compute_performance-ego0": 0.007022209707445944}}
set_robot_commands_max0.009512071506268377
set_robot_commands_mean0.008660345847759517
set_robot_commands_median0.008726048628356832
set_robot_commands_min0.007677214628056026
sim_compute_performance-ego0_max0.007594122874746712
sim_compute_performance-ego0_mean0.007087422350264906
sim_compute_performance-ego0_median0.007098974236640009
sim_compute_performance-ego0_min0.0065576180530328935
sim_compute_sim_state_max0.0352780584689481
sim_compute_sim_state_mean0.02682572945667048
sim_compute_sim_state_median0.026797343848845447
sim_compute_sim_state_min0.01843017166004292
sim_render-ego0_max0.01113991038586873
sim_render-ego0_mean0.010651520547223623
sim_render-ego0_median0.01072706400008126
sim_render-ego0_min0.010012043802863257
simulation-passed1
step_physics_max0.2845895068036031
step_physics_mean0.2373486266445856
step_physics_median0.23427572248381048
step_physics_min0.19625355480711823
survival_time_max59.99999999999873
survival_time_mean59.99999999999873
survival_time_min59.99999999999873
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6205313649Jean-Sébastien Grondin 🇨🇦exercise_ros_templateaido-LFV-sim-validation354host-errornoreg020:06:13
The container "evalu [...]
The container "evaluator" exited with code 1.


Look at the logs for the container to know more about the error.
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6205013593Andras Beres202-1aido-LFV_multi-sim-validation356host-errornoreg020:09:10
InvalidEnvironment: [...]
InvalidEnvironment:
Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego3" aborted with the following error:

error in ego3 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "submission.py", line 60, in init
              ||     self.agent = self.create_agent(self.env)
              ||   File "submission.py", line 162, in create_agent_dagger
              ||     agent = DaggerAgent.load_from_checkpoint(
              ||   File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/device_dtype_mixin.py", line 124, in cuda
              ||     return super().cuda(device=device)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in cuda
              ||     return self._apply(lambda t: t.cuda(device))
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 381, in _apply
              ||     param_applied = fn(param)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in <lambda>
              ||     return self._apply(lambda t: t.cuda(device))
              || RuntimeError: CUDA error: out of memory
              ||

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_challenges/cie_concrete.py", line 681, in scoring_context
    yield cie
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 68, in go
    wrap(cie)
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/experiment_manager.py", line 34, in wrap
    asyncio.run(main(cie, logdir, attempts), debug=True)
  File "/usr/local/lib/python3.8/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/usr/local/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
    return future.result()
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 248, in main
    raise InvalidEnvironment(msg) from e
duckietown_challenges.exceptions.InvalidEnvironment: Detected out of CUDA memory:

Traceback (most recent call last):
  File "/usr/local/lib/python3.8/site-packages/duckietown_experiment_manager/code.py", line 242, in main
    robot_ci.write_topic_and_expect_zero("seed", config.seed)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 143, in write_topic_and_expect_zero
    msgs = read_reply(self.fpout, timeout=timeout, nickname=self.nickname)
  File "/usr/local/lib/python3.8/site-packages/zuper_nodes_wrapper/wrapper_outside.py", line 309, in read_reply
    raise RemoteNodeAborted(msg)
zuper_nodes.structures.RemoteNodeAborted: The remote node "ego3" aborted with the following error:

error in ego3 |Unexpected error:
              |
              || Traceback (most recent call last):
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/wrapper.py", line 322, in loop
              ||     call_if_fun_exists(node, "init", context=context_data)
              ||   File "/usr/local/lib/python3.8/dist-packages/zuper_nodes_wrapper/utils.py", line 21, in call_if_fun_exists
              ||     f(**kwargs)
              ||   File "submission.py", line 60, in init
              ||     self.agent = self.create_agent(self.env)
              ||   File "submission.py", line 162, in create_agent_dagger
              ||     agent = DaggerAgent.load_from_checkpoint(
              ||   File "/usr/local/lib/python3.8/dist-packages/pytorch_lightning/utilities/device_dtype_mixin.py", line 124, in cuda
              ||     return super().cuda(device=device)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in cuda
              ||     return self._apply(lambda t: t.cuda(device))
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 359, in _apply
              ||     module._apply(fn)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 381, in _apply
              ||     param_applied = fn(param)
              ||   File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 463, in <lambda>
              ||     return self._apply(lambda t: t.cuda(device))
              || RuntimeError: CUDA error: out of memory
              ||
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