osim-rl-grader

This project provides a grader for the RL task in crowdAI. It is based on openai/gym-http-api

Installation

conda create -n opensim-rl -c kidzik opensim
activate opensim-rl
conda install -c conda-forge lapack
conda install flask git
pip install git+https://github.com/kidzik/osim-rl.git
git clone https://github.com/kidzik/osim-rl-grader.git
cd osim-rl-grader
cp localsettings.py.example localsettings.py
# Now shoot up your favourite editor, open localsettings.py and
# fill in the details about the associated CrowdAI Server and
# the ChallengeID and the Grader Authentication Token.

## Instructions for headless simulations
sudo apt-get install xpra
sudo apt-get install imagemagick
# Start the xpra server by :
xpra --xvfb="Xorg -noreset -nolisten tcp +extension GLX \
    -config /etc/xpra/xorg.conf \
    +extension RANDR +extension RENDER -logfile ${HOME}/.xpra/Xorg-10.log" \
    start :100    
# Now configure DISPLAY variable in localsettings to ":100" (you can set it to whatever DISPLAY var works for you)

Getting started

To start the server from the command line, run this:

python gym_http_server.py -p 80 -l 127.0.0.1

API specification

• POST /v1/envs/

  • Create an instance of the specified environment
  • param: env_id -- gym environment ID string, such as 'CartPole-v0'
  • param: token -- token from crowdAI
  • returns: token -- a short identifier (such as '3c657dbc') for the created environment instance. The token is used in future API calls to identify the environment to be manipulated

• POST /v1/envs/<token>/reset/

  • Reset the state of the environment and return an initial observation.
  • param: token -- token from crowdAI for the environment instance
  • returns: observation -- the initial observation of the space

• POST /v1/envs/<token>/step/

  • Step though an environment using an action.
  • param: token -- token from crowdAI
  • param: action -- an action to take in the environment
  • returns: observation -- agent's observation of the current environment
  • returns: reward -- amount of reward returned after previous action
  • returns: done -- whether the episode has ended
  • returns: info -- a dict containing auxiliary diagnostic information

• POST /v1/envs/<token>/monitor/start/

  • Start monitoring
  • param: token -- token from crowdAI
  • param: force (default=False) -- Clear out existing training data from this directory (by deleting every file prefixed with "openaigym.")
  • param: resume (default=False) -- Retain the training data already in this directory, which will be merged with our new data
  • (NOTE: the video_callable parameter from the native env.monitor.start function is NOT implemented)

• POST /v1/envs/<token>/monitor/close/

  • Flush all monitor data to disk
  • param: token -- token from crowdAI