A framework for learning about and experimenting with reinforcement learning algorithms
Project description
===============================
RLFlow
===============================
.. image:: https://img.shields.io/pypi/v/rlflow.svg
:target: https://pypi.python.org/pypi/rlflow
.. image:: https://img.shields.io/travis/tpbarron/rlflow.svg
:target: https://travis-ci.org/tpbarron/rlflow
.. image:: https://readthedocs.org/projects/rlflow/badge/?version=latest
:target: https://rlflow.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/tpbarron/rlflow/shield.svg
:target: https://pyup.io/repos/github/tpbarron/rlflow/
:alt: Updates
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
------------------
* Errors / warnings on TensorFlow session save
License
------------------
* Free software: MIT license
* Documentation: https://rlflow.readthedocs.io.
=======
History
=======
0.1.2/3 (2016-17-15)
------------------
* Improving meta data and fixing __init__ scripts to load subpackages properly
0.1.0 (2016-16-15)
------------------
* First release on PyPI.
RLFlow
===============================
.. image:: https://img.shields.io/pypi/v/rlflow.svg
:target: https://pypi.python.org/pypi/rlflow
.. image:: https://img.shields.io/travis/tpbarron/rlflow.svg
:target: https://travis-ci.org/tpbarron/rlflow
.. image:: https://readthedocs.org/projects/rlflow/badge/?version=latest
:target: https://rlflow.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://pyup.io/repos/github/tpbarron/rlflow/shield.svg
:target: https://pyup.io/repos/github/tpbarron/rlflow/
:alt: Updates
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
------------------
* Errors / warnings on TensorFlow session save
License
------------------
* Free software: MIT license
* Documentation: https://rlflow.readthedocs.io.
=======
History
=======
0.1.2/3 (2016-17-15)
------------------
* Improving meta data and fixing __init__ scripts to load subpackages properly
0.1.0 (2016-16-15)
------------------
* First release on PyPI.
Project details
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