capybaralet / dqn

This is a very basic DQN implementation, which uses OpenAI's gym environment and Keras/Theano neural networks.

dqn

This is a very basic DQN (with experience replay) implementation, which uses OpenAI's gym environment and Keras/Theano neural networks.

Requirements

• gym
• keras
• theano
• numpy

and all their dependencies.

Usage

To run, python example.py <env_name>. It runs MsPacman-v0 if no env is specified. Uncomment the env.render() line to see the game while training, however, this is likely to make training slow.

Currently, it assumes that the observation is an image, i.e. a 3d array, which is the case for all Atari games, and other Atari-like environments.

Purpose

This is meant to be a very simple implementation, to be used as a starter code. I aimed it to be easy-to-comprehend rather than feature-complete.

Pull requests welcome!

References

• https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

TODO

• Extend to other environemnts. Currently only works for Atari and Atari-like environments where the observation space is a 3D Box.