Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) (Hardcover)

by Richard S. Sutton (Author), Andrew G. Barto (Author)

Editorial Reviews

Review
"This is a groundbreaking work, dealing with a subject that you would have expected to have been sorted out right at the start of AI... This isn't a simple theory but many of the ideas and methods are practically useful and if you have an interest in neural networks or learning systems then you need to study this book for the six months it deserves!" -- Mike James, Computer Shopper, November 1998

Product Description
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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Product Details

• Hardcover: 322 pages
• Publisher: The MIT Press (March 1, 1998)
• Language: English
• ISBN-10: 0262193981
• ISBN-13: 978-0262193986
• Product Dimensions: 9.1 x 7.2 x 1.2 inches
• Shipping Weight: 1.8 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars See all reviews (14 customer reviews)
• Amazon.com Sales Rank: #104,384 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

17 of 19 people found the following review helpful:

5.0 out of 5 stars An excellent introduction to Reinforcement Learning, February 29, 2000

By	David Tan (USA) - See all my reviews

This is probably one of the best book I have read in the past 1 year.

The authors present the subject with an excellent balance of mathematical, computational and intuitive material. The book also includes plenty of real-life examples to explain the concepts and motivations for the algorithms.

The book starts with examples and intuitive introduction and definition of reinforcement learning. It follows with 3 chapters on the 3 fundamental approaches to reinforcement learning: Dynamic programming, Monte Carlo and Temporal Difference methods. Subsequent chapters build on these methods to generalize to a whole spectrum of solutions and algorithms.

The book is very readable by average computer students. Possibly the only difficult one is chapter 8, which deals with some neural network concepts.

I highly recommend this book to anyone who wants to learn about this subject.

12 of 13 people found the following review helpful:

5.0 out of 5 stars Excellent introduction to reinforcement learning, August 3, 2003

By	Mihailo Despotovic (Silicon Valley, CA USA) - See all my reviews (REAL NAME)

I have this book more than a year now and I am going through it for the second time, so I think I have a pretty good picture about it.

The book consists of three parts. In the first part, "The Problem", the authors define the scope of issues reinfocement learning is dealing with and they give some interesting introductory examples. Then, they move on to the concept of evaluative feedback and, eventually, define the reinforcement learning problem formally.

The second part, "Elementary Solution Methods" consists of three more-less independent subparts: Dynamic Programming, Monte Carlo Methods and Temporal Difference Learning. All three fundamental reinforcement learning methods are presented in an interesting way and using good examples. Personally, I liked the TD-Learning part best and I agree that this method is indeed the central method and an original contribution of reinforecement learning to the field of machine learning.

The third part, "A Unified View" present more advanced techniques. The last chapter gives the most important case studies in reinforcement learning including Samuel's Checkers Player and Thesauro's TD-Gammon.

The book is very readable and every chapter ends with illustrative exercises (many of them actually are real programming projects!), always useful summary and very valuable bibliographical and historical remarks. Some subchapters are more advanced and therefore marked with '*'. I really recommend first two parts to any student ofd computer science or anyone interested in machine learning and fuzzy computing. The third part is much more advanced but it would be definitely interesting for advanced computer scientists and graduate students.

This is still the first edition of the book which means that the material is almost six years old, but it's the third printing, so there is lot of interest and I would suggest (for second edition) that authors include solutions to (at least selected) exercises, something like Knuth did in "The Art of Computer Programming".

12 of 14 people found the following review helpful:

5.0 out of 5 stars Its a nice introductory text on Reinforcement Leaning!, January 7, 1999

By	[e-mail] Abhijit Gosavi (Tampa, Florida) - See all my reviews

The book is easy and interesting to read. The diagrams, especially those on TD, throw a great deal of insight on the basic concept of TD. The intuitive ideas behind RL are developed clearly. At the same time all the fundamental concepts are made mathematically precise using very simple language and notation. Anybody new to RL should find this book extremely useful.