Reinforcement learning is an area of machine learning and computer science concerned with how to select an action in a state that maximizes a numerical reward in a particular environment.
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Reinforcement learning is learning what to do--how to map situations to actions--so as to maximize a numerical reward signal. The learner is not told which actions to take, as in most forms of machine learning, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards. These two characteristics--trial-and-error search and delayed reward--are the two most important distinguishing features of reinforcement learning.
From Reinforcement Learning: An Introduction
Significant Literature
- Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto, MIT Press, Cambridge, MA, 1998
External Links
- Reinforcement Learning (wikipedia)
- Temporal Difference Learning
- Q-Learning
- SARSA
- Temporal-Difference
- Monte Carlo
- Dynamic programming
- Optimal Control
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