ChainerRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework.
ChainerRL is tested with Python 2.7+ and 3.5.1+. For other requirements, see requirements.txt.
ChainerRL can be installed via PyPI:
pip install chainerrl
It can also be installed from the source code:
python setup.py install
- Windows user
ChainerRL contains atari_py as dependencies, and windows users may face error while installing it.
This problem is discussed in OpenAI gym issues,
and one possible counter measure is to enable "Bash on Ubuntu on Windows" for Windows 10 users.
Refer Official install guilde to install "Bash on Ubuntu on Windows".
You can try ChainerRL Quickstart Guide first, or check the examples ready for Atari 2600 and Open AI Gym.
| Algorithm | Discrete Action | Continous Action | Recurrent Model | CPU Async Training |
|---|---|---|---|---|
| DQN (including DoubleDQN etc.) | o | o (NAF) | o | x |
| DDPG | x | o | o | x |
| A3C | o | o | o | o |
| ACER | o | x | o | o |
| NSQ (N-step Q-learning) | o | o (NAF) | o | o |
Following algorithms have been implemented in ChainerRL:
- A3C (Asynchronous Advantage Actor-Critic)
- ACER (Actor-Critic with Experience Replay) (only the discrete-action version for now)
- Asynchronous N-step Q-learning
- DQN (including Double DQN, Persistent Advantage Learning (PAL), Double PAL, Dynamic Policy Programming (DPP))
- DDPG (Deep Deterministic Poilcy Gradients) (including SVG(0))
- PGT (Policy Gradient Theorem)
Q-function based algorithms such as DQN can utilize a Normalized Advantage Function (NAF) to tackle continuous-action problems as well as DQN-like discrete output networks.
Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used.
Any kind of contribution to ChainerRL would be highly appreciated! If you are interested in contributing to ChainerRL, please read CONTRIBUTING.md.