Metadata-Version: 1.1
Name: rlflow
Version: 0.1.3
Summary: A framework for learning about and experimenting with reinforcement learning algorithms
Home-page: https://github.com/tpbarron/rlflow
Author: Trevor Barron
Author-email: barron.trevor@gmail.com
License: MIT license
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.
        
Keywords: FLFlow,TFLearn,TensorFlow,Deep Learning,Reinforcement Learning,Machine Learning,Neural Networks
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
