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Measure sample efficiency and generalization in reinforcement learning using procedurally generated environments
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Classify images of snake species from around the world
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A new benchmark for Artificial Intelligence (AI) research in Reinforcement Learning
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Unity Obstacle Tower Challenge
Evalutation error : Unity environment took too long to respond
About 5 years agoIn local docker, it always tested well.
I am getting same error for every submission.
The Unity environment took too long to respond.
Problem on agent
About 5 years agoIt runs correctly in my local machines.
Also, set realtime_mode=True
doesnβt help.
Testing agent in local with docker
About 5 years agoI followed the instruction in README.md
.
I successfully built docker image.
I run docker image with tow terminals as described in Run Docker image section.
The agent looks good and it waits for the environment.
When I run the environment, anything happens.
Below are my console messages for both.
Agent
root
INFO:mlagents_envs:Start training by pressing the Play button in the Unity Editor.
Traceback (most recent call last):
File "run.py", line 27, in <module>
env = ObstacleTowerEnv(args.environment_filename, docker_training=args.docker_training)
File "/srv/conda/lib/python3.6/site-packages/obstacle_tower_env.py", line 45, in __init__
timeout_wait=timeout_wait)
File "/srv/conda/lib/python3.6/site-packages/mlagents_envs/environment.py", line 69, in __init__
aca_params = self.send_academy_parameters(rl_init_parameters_in)
File "/srv/conda/lib/python3.6/site-packages/mlagents_envs/environment.py", line 491, in send_academy_parameters
return self.communicator.initialize(inputs).rl_initialization_output
File "/srv/conda/lib/python3.6/site-packages/mlagents_envs/rpc_communicator.py", line 80, in initialize
"The Unity environment took too long to respond. Make sure that :\n"
mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :
The environment does not need user interaction to launch
The Academy and the External Brain(s) are attached to objects in the Scene
The environment and the Python interface have compatible versions.
Environment
+ ENV_PORT=
+ ENV_FILENAME=
+ '[' -z '' ']'
+ ENV_PORT=5005
+ '[' -z '' ']'
+ ENV_FILENAME=/home/otc/ObstacleTower/obstacletower.x86_64
+ touch otc_out.json
+ APP_PID=7
+ xvfb-run --auto-servernum '--server-args=-screen 0 640x480x24' /home/otc/ObstacleTower/obstacletower.x86_64 --port 5005 2
+ TAIL_PID=8
+ wait 7
+ tail -f otc_out.json
Problem on agent
About 5 years agoI tested the Obstacle tower environment with local machines.
I confirmed that the action space is consist of 4 numbers in list, like [0, 0, 0, 1]
I submitted a starter kit agent for a test, and it evaluated successfully.
Then, I tested my agent for submission which slightly modified from starter kit.
The modification was to force jump action 0 from env.action_space.sample()
Actual source code is below. It is part of run.py
in run_episode(env) function
.
while not done:
action = env.action_space.sample()
action[2] = 0
obs, reward, done, info = env.step(action)
From evaluation log, it stuck at step 0.
It is my first try to participate in this kind of challenges, therefore I am not familiar with the environment.
What is the problem with my code?
Submissions Q&A
About 5 years agoIt seems evaluation server dead.
Mine stuck for 7 hours