Deep Reinforcement Learning for Keras

What is it?

keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras.

Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy.

Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. Documentation is available online.

What is included?

As of today, the following algorithms have been implemented:

Deep Q Learning (DQN) [1], [2]
Double DQN [3]
Deep Deterministic Policy Gradient (DDPG) [4]
Continuous DQN (CDQN or NAF) [6]
Cross-Entropy Method (CEM) [7], [8]
Dueling network DQN (Dueling DQN) [9]
Deep SARSA [10]
Asynchronous Advantage Actor-Critic (A3C) [5]
Proximal Policy Optimization Algorithms (PPO) [11]

You can find more information on each agent in the doc.

Installation

Install Keras-RL from Pypi (recommended):

pip install keras-rl

Install from Github source:

git clone https://github.com/keras-rl/keras-rl.git
cd keras-rl
python setup.py install

Examples

If you want to run the examples, you'll also have to install:

gym by OpenAI: Installation instruction
h5py: simply run pip install h5py

For atari example you will also need:

Pillow: pip install Pillow
gym[atari]: Atari module for gym. Use pip install gym[atari]

Once you have installed everything, you can try out a simple example:

python examples/dqn_cartpole.py

This is a very simple example and it should converge relatively quickly, so it's a great way to get started! It also visualizes the game during training, so you can watch it learn. How cool is that?

Some sample weights are available on keras-rl-weights.

If you have questions or problems, please file an issue or, even better, fix the problem yourself and submit a pull request!

Citing

If you use keras-rl in your research, you can cite it as follows:

@misc{plappert2016kerasrl,
    author = {Matthias Plappert},
    title = {keras-rl},
    year = {2016},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/keras-rl/keras-rl}},
}

References

Playing Atari with Deep Reinforcement Learning, Mnih et al., 2013
Human-level control through deep reinforcement learning, Mnih et al., 2015
Deep Reinforcement Learning with Double Q-learning, van Hasselt et al., 2015
Continuous control with deep reinforcement learning, Lillicrap et al., 2015
Asynchronous Methods for Deep Reinforcement Learning, Mnih et al., 2016
Continuous Deep Q-Learning with Model-based Acceleration, Gu et al., 2016
Learning Tetris Using the Noisy Cross-Entropy Method, Szita et al., 2006
Deep Reinforcement Learning (MLSS lecture notes), Schulman, 2016
Dueling Network Architectures for Deep Reinforcement Learning, Wang et al., 2016
Reinforcement learning: An introduction, Sutton and Barto, 2011
Proximal Policy Optimization Algorithms, Schulman et al., 2017

MSardelich/keras-rl