Reinforcement Learning

This page contains resources about Reinforcement Learning.

Subfields and Concepts

 * Multi-Armed Bandit
 * Finite Markov Decision Process
 * Temporal-Difference Learning
 * Q-Learning
 * Adaptive Dynamic Programming
 * Deep Reinforcement Learning
 * Connectionist Reinforcement Learning
 * Score function estimator / REINFORCE
 * Variance Teduction Techniques (VRT) for gradient estimates

Lectures Notes

 * Reinforcement Learning by David Silver
 * Reinforcement Learning by Michael Herrmann

Books and Book Chapters

 * Russell, S. J., & Norvig, P. (2010). "Chapter 21: Reinforcement Learning". Artificial Intelligence: A Modern Approach. Prentice Hall.
 * Alpaydin, E. (2010). "Chapter 18: Reinforcement Learning". Introduction to machine learning. MIT Press.
 * Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.

Scholarly Articles

 * Szepesvári, C. (2010). Algorithms for Reinforcement Learning. Synthesis Lectures on Artificial Intelligence and Machine , 4(1), 1-103.
 * Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research, 4, 237-285.
 * Mitchell, T. M. (1997). "Chapter 13: Reinforcement Learning". Machine Learning. McGraw Hill.
 * Wl, M. H., Harmon, M. E., & Harmon, S. S. (1996). Reinforcement Learning: A Tutorial.

Software

 * PyBrain - Python
 * OpenAI Gym - A toolkit for developing and comparing Reinforcement Learning algorithms
 * Reinforcement-Learning-Toolkit - Python
 * DeepRL - Python

Other resources

 * Awesome-RL (Github) - A curated list of Reinforcement Learning resources
 * Practical_RL - A course in reinforcement learning in the wild
 * Deep RL Bootcamp - Videos and slides
 * Software Tools for RL, ANNs and Robotics - Python and MATLAB
 * Reinforcement Learning - blog post
 * Learning Diverse Skills via Maximum Entropy Deep Reinforcement Learning - blog post