Framework for deep reinforcement learning
DeeR is a python library for Deep Reinforcement. It is build with modularity in mind so that it can easily be adapted to any need. It provides many possibilities out of the box (prioritized experience replay, double Q-learning, DDPG, etc). Many different environment examples are also provided (some of them using OpenAI gym).
This framework is tested to work under Python 2.7, and Python 3.5. It should also work with Python 3.3 and 3.4.
The required dependencies are NumPy >= 1.10, joblib >= 0.9. You also need theano >= 0.8 or tensorflow >= 0.9 along with the keras library.
For running the examples, Matplotlib >= 1.1.1 is required. For running the atari games environment, you need to install ALE >= 0.4.
The documentation is available at : http://deer.readthedocs.io/
Here are a few examples :
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|deer-0.3.1-py2-none-any.whl (166.7 kB) Copy SHA256 hash SHA256||Wheel||py2||Jun 26, 2017|
|deer-0.3.1.tar.gz (64.9 kB) Copy SHA256 hash SHA256||Source||None||Jun 26, 2017|