ISBN-10:
0262039249
ISBN-13:
9780262039246
Pub. Date:
11/13/2018
Publisher:
MIT Press
Reinforcement Learning: An Introduction / Edition 2

Reinforcement Learning: An Introduction / Edition 2

by Richard S. Sutton, Andrew G. BartoRichard S. Sutton
Current price is , Original price is $80.0. You

Temporarily Out of Stock Online

Please check back later for updated availability.

Overview

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Product Details

ISBN-13: 9780262039246
Publisher: MIT Press
Publication date: 11/13/2018
Series: Adaptive Computation and Machine Learning series
Edition description: second edition
Pages: 552
Sales rank: 101,166
Product dimensions: 7.20(w) x 9.10(h) x 1.50(d)
Age Range: 18 Years

About the Author

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.

What People are Saying About This

Dana Ballard

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.

Wolfram Schultz

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.

Endorsement

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

From the Publisher

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

Dimitri P. Bertsekas and John N. Tsitsiklis

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.

Toshio Fukuda

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.

Customer Reviews