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.
- A brief encyclopedia article on reinforcement learning at Wikipedia
- F. Woergoetter and B. Porr (2008),
Reinforcement Learning, Scholarpedia, 3(3):1448.
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
- R. Sutton and A. Barto. Reinforcement learning: An introduction
Cambridge, MA, MIT Press (1998),
Classic introductory textbook to RL. Highly Recommended.
It requires no background knowledge, describes the key ideas, open problems, and great applications of this field.
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- Annotated Book and Course recommendations for students and researchers