This software is a python implementation of Deep Q-Networks for playing ATARI games with Chainer package.
I followed the implementation described in:
- V. Mnih et al., "Playing atari with deep reinforcement learning"
- V. Mnih et al., "Human-level control through deep reinforcement learning"
For japanese instruction of DQN and historical review, please check:
My implementation is dependent on RL-glue, Arcade Learning Environment, and Chainer. To run the software, you need following softwares/packages.
- Python 2.7+
- Pillow (PIL)
- Chainer (1.3.0): https://github.com/pfnet/chainer
- RL-glue core: https://sites.google.com/a/rl-community.org/rl-glue/Home/rl-glue
- RL-glue Python codec: https://sites.google.com/a/rl-community.org/rl-glue/Home/Extensions/python-codec
- Arcade Learning Environment (version ALE 0.4.4): http://www.arcadelearningenvironment.org/
This software was tested on Ubuntu 14.04 LTS.
How to run
Please check readme.txt