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Reinforcement Learning – An Introduction (Adaptive Computation and Machine Learning series)

Reinforcement Learning – An Introduction (Adaptive Computation and Machine Learning series)

by Francis Bach (Author), Francis Bach (Author), Richard S. Sutton (Author)

  • New

    Hardcover 1998

    $47.49
Synopsis

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 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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More Information

Format: Hardcover
 Pages: 344
 Edition: second edition
 Publisher: MIT Press
Published: 08 May 1998
ISBN 10: 0262193981
ISBN 13: 9780262193986
Book Overview: This is a highly intuitive and accessible introduction to the recent major developments in reinforcement learning, written by two of the field's pioneering contributors. -- Dimitri P. Bertsekas and John N. Tsitsiklis, Professors, Department of Electrical Engineering andn Computer Science, Massachusetts Institute of Technology This book not only provides an introduction to learning theory but also serves as a tremendous source of ideas for further development and applications in the real world. -- Toshio Fukuda, Nagoya University, Japan; President, IEEE Robotics and Automantion Society Reinforcement learning has always been important in the understanding of the driving force behind biological systems, but in the last two decades it has become increasingly important, owing to the development of mathematical algorithms. Barto and Sutton were the prime movers in leading the development of these algorithms and have described them with wonderful clarity in this new text. I predict it will be the standard text. -- Dana Ballard, Professor of Computer Science, University of Rochester The widely acclaimed work of Sutton and Barto on reinforcement learning applies some essentials of animal learning, in clever ways, to artificial learning systems. This is a very readable and comprehensive account of the background, algorithms, applications, and future directions of this pioneering and far-reaching work. -- Wolfram Schultz, University of Fribourg, Switzerland

Author Bio
Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind. Andrew G. Barto is Professor Emeritus in the College of Computer and Information Sciences at the University of Massachusetts Amherst.