RLFlow
===============================
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A framework for learning about and experimenting with reinforcement learning algorithms.
It is built on top of TensorFlow and `TFLearn <http://tflearn.org/>`_ and is interfaces
with the OpenAI gym (universe should work, too). It aims to be as modular as possible so
that new algorithms and ideas can easily be tested. I started it to gain a better
understanding of core RL algorithms and maybe it can be useful for others as well.
Features
--------
Algorithms (future algorithms italicized):
- MDP algorithms
+ Value iteration
+ Policy iteration
- Temporal Difference Learning
+ SARSA
+ Deep Q-Learning
+ *Policy gradient Q-learning*
- Gradient algorithms
+ Vanilla policy gradient
+ *Deterministic policy gradient*
+ *Natural policy gradient*
- Gradient-Free algorithms
+ *Cross entropy method*
Function approximators (defined by TFLearn model):
- Linear
- Neural network
- *RBF*
Works with any OpenAI gym environment.
Future Enhancements
-------------------
* Improved TensorBoard logging
* Improved model snapshotting to include exploration states, memories, etc.
* Any suggestions?
Fixes
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* Errors / warnings on TensorFlow session save
License
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* Free software: MIT license
* Documentation: https://rlflow.readthedocs.io.
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History
=======
0.1.2/3 (2016-17-15)
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* Improving meta data and fixing __init__ scripts to load subpackages properly
0.1.0 (2016-16-15)
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* First release on PyPI.
1 maintainer
Metadata
1 maintainer
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