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rl's Introduction

About
-----
Reinforcement learning in python.

This code is intended mainly as proof of concept of the algorithms presented in 
[1]. The implementations are not particularly clear, efficient, well tested or 
numerically stable. We advise against using this software for nondidactic 
purposes.

This software is licensed under the MIT License. 

Features
--------
Agents:
    Model-based (value and policy iteration)
    Model-free (Monte Carlo, Sarsa, Q-learning and variations)
    Model-building (Dyna-Q)

Examples
--------
See the examples directory.

References
----------
[1] Sutton, R.S. and Barto, A.G. Reinforcement Learning: An Introduction.
 MIT Press, 1998.
    

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