dotd/docker_rl

OpenAI gym Docker Image

This Docker image comes with openai and pytorch-cpu. This allows users to start working on reinforcement learning in a couple of easy steps.

This image is particularly ideal for Windows users, for whom openai gym is not easily installed.

Current features:

• cpu enabled pytorch
• classic control gym environments
• box2d environments
• Recording of environments

To be developed:

• Automated environment recording (no manual command entry)
• environment rendering
• Atari environments
• cpu enabled tensorflow

How to use

1. Either
   • Clone the repo and build the image: docker build --tag=image_name .
   • pull the image: docker pull ttitcombe/rl_pytorch:latest
2. Launch the container: docker run -it --name=container_name image_name python. This should enter the python interpreter.
3. Before entering the python interpreter, a script to attach the graphical display should have been run.
4. If you want to re-enter the container and record, you can run /usr/local/bin/startup_script.py as the CMD, e.g. docker exec -it container_name /usr/local/bin/startup_script.py. This should enter a bash script.
   Alternatively, you can open a bash script and run it from there.
5. If you want to re-enter the container, record, AND run something, try /usr/local/bin/startup_script.py "python /path/to/my/file.py" as your CMD.
6. If you don't care about recording, use /bin/bash to enter bash or python to enter the python interpreter.

To test that the container works, try recording an environment:

import gym
import torch

env_to_wrap = gym.make("LunarLander-v1")
env = gym.wrappers.Monitor(env_to_wrap, "someDir")
frame = env.reset()
is_done = False
while not is_done:
  action = env.action_space.sample()
  _, _, is_done, _ = env.step(action)
env.close()
env_to_wrap.close()

If this doesn't throw an error, then congratulations, you can record OpenAI gym!

You can extract the recording once you are outside of the container with the command docker cp container_name:/path/to/my/file local/path/to/file.

# docker_rl

docker_rl