keras-rl2 implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras.
keras-rl2 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-rl2 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.
As of today, the following algorithms have been implemented:
You can find more information on each agent in the doc.
pip install keras-rl2
git clone https://github.com/wau/keras-rl2.git cd keras-rl python install .
If you want to run the examples, you'll also have to install:
pip install h5py
For atari example you will also need:
pip install Pillow
pip install gym[atari]
Once you have installed everything, you can try out a simple example:
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?
If you have questions or problems, please file an issue or, even better, fix the problem yourself and submit a pull request!