Reinforcement learning : an introduction
- 2nd ed.
- Cambridge, Massachusetts : MIT Press, 2018
- xxii, 526 p. ; ill., 24 cm.
- Adaptive computation and machine learning series .
Includes bibliographical references and index.
In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.--Jacket. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms.-- Provided by publisher.
9780262039246 (hbk)
Artificial Intelligence Bellman equation Dynamic programming Function approximation Monte Carlo methods Markov property Q-learning