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Reinforcement Learning by Richard S. Sutton
Book DescriptionReinforcement 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 when interacting with a complex, uncertain environment. 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. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
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Book DetailsISBN: 9780262193986
(229mm x 178mm x 20mm)
Imprint: MIT Press
Publisher: MIT Press Ltd
Publish Date: 8-May-1998
Country of Publication: United States
Books By Author Richard S. Sutton
Neural Networks for Control, Paperback (April 1995)» View all books by Richard S. Sutton
This work brings together examples of all the most important paradigms for the application of neural networks to robotics and control. It covers general principles and the areas of motion control and applications domains.
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Author Biography - Richard S. Sutton
Andrew G. Barto is Professor of Computer Science at the University of Massachusetts.
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