Metadata-Version: 2.1
Name: introrl
Version: 0.0.2
Summary: IntroRL provides a framework for exploring Reinforcement Learning.
Home-page: http://introrl.readthedocs.org/en/latest/
Author: Charlie Taylor
Author-email: cet@appliedpython.com
License: GPL-3
Download-URL: https://github.com/sonofeft/IntroRL
Keywords: introrl setuptools development
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Provides-Extra: test
Provides-Extra: dev
Requires-Dist: future
Provides-Extra: dev
Requires-Dist: check-manifest; extra == 'dev'
Provides-Extra: test
Requires-Dist: coverage; extra == 'test'


.. image:: https://travis-ci.org/sonofeft/IntroRL.svg?branch=master
    :target: https://travis-ci.org/sonofeft/IntroRL

.. image:: https://img.shields.io/pypi/v/IntroRL.svg
    :target: https://pypi.python.org/pypi/introrl

.. image:: https://img.shields.io/pypi/pyversions/IntroRL.svg
    :target: https://wiki.python.org/moin/Python2orPython3

.. image:: https://img.shields.io/pypi/l/IntroRL.svg
    :target: https://pypi.python.org/pypi/introrl


**IntroRL** Provides A Framework For Exploring Reinforcement Learning.

It is based on the textbook
`"Reinforcement Learning An Introduction" <https://www.amazon.com/Reinforcement-Learning-Introduction-Adaptive-Computation/dp/0262039249>`_ 
By Sutton & Barto.

The textbook is also available in `PDF format at the authors' site. <http://incompleteideas.net/book/the-book-2nd.html>`_

This documentation of **IntroRL** is organized around the chapter structure of the Sutton & Barto textbook.

Many of the examples and figures are reproduced here in order to validate the **IntroRL** code.

There is another site by `Shangtong Zhang <https://github.com/ShangtongZhang/reinforcement-learning-an-introduction>`_
that was of great help to me and which covers many areas of the textbook not covered here.

===================================================================================================================================================


See the Code at: `<https://github.com/sonofeft/IntroRL>`_

See PyPI page at:`<https://pypi.python.org/pypi/introrl>`_




