What is Reinforcement Learning?

Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize its long-term reward. It is a very general framework, and as such, has interconnections with many fields, encompassing

• computer science (artificial intelligence, machine learning),
• engineering (information theory, adaptive control),
• economics (rational agents, game theory),
• mathematics (statistics),
• psychology (behaviorism, motivation, incentives),
• philosophy (inductive inference, theory of knowledge).

Reinforcement learning (augmented by ideas from information theory) can provide a unified theory for artificial intelligence, and is key to accomplishing more robust and flexible solutions, e.g. in computer games and autonomous robots.

Introductory Literature

• A brief encyclopedia article on reinforcement learning at Wikipedia
  http://en.wikipedia.org/wiki/Reinforcement_learning

• F. Woergoetter and B. Porr (2008),
  Reinforcement Learning, Scholarpedia, 3(3):1448.
  http://www.scholarpedia.org/article/Reinforcement_learning
  A comparison of the machine learning and neural network perspective of RL.

• L. P. Kaelbling and M. L. Littman and A. W. Moore,
  Reinforcement learning: A Survey,
  Journal of Artificial Intelligence Research, 4 (1996) 237—285
  http://www.cs.cmu.edu/afs/cs.cmu.edu/project/jair/pub/volume4/kaelbling96a.pdf

• R. Sutton and A. Barto. Reinforcement learning: An introduction
  Cambridge, MA, MIT Press (1998),
  http://www-anw.cs.umass.edu/~rich/book/the-book.html
  Classic introductory textbook to RL. Highly Recommended.
  It requires no background knowledge, describes the key ideas, open problems, and great applications of this field.

A longer list of recent and classical papers on Reinforcement Learning is here

Other Resources

• RL mailing list
  http://groups.google.com/group/rl-list

• RL&AI links by Rich Sutton
  http://rlai.cs.ualberta.ca/RLAI/rlai.html

• Annotated Book and Course recommendations for students and researchers
  http://www.hutter1.net/ai/introref.htm