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

Online Courses

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

See also

• Monte Carlo Methods
• Variational Bayesian Inference
• Control Theory

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