A framework for learning about and experimenting with reinforcement learning algorithms
A framework for learning about and experimenting with reinforcement learning algorithms. It is built on top of TensorFlow and TFLearn 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.
Algorithms (future algorithms italicized):
- Value iteration
- Policy iteration
Temporal Difference Learning
- Deep Q-Learning
- Policy gradient Q-learning
- Vanilla policy gradient
- Deterministic policy gradient
- Natural policy gradient
- Cross entropy method
Function approximators (defined by TFLearn model):
- Neural network
Works with any OpenAI gym environment.
- Improved TensorBoard logging
- Improved model snapshotting to include exploration states, memories, etc.
- Any suggestions?
- Errors / warnings on TensorFlow session save
- Free software: MIT license
- Documentation: https://rlflow.readthedocs.io.
- First release on PyPI.
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