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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) Hardcover – March 1, 1998

ISBN-13: 978-0262193986 ISBN-10: 9780262193986

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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) + Pattern Recognition and Machine Learning (Information Science and Statistics) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 322 pages
  • Publisher: A Bradford Book (March 1, 1998)
  • Language: English
  • ISBN-10: 9780262193986
  • ISBN-13: 978-0262193986
  • ASIN: 0262193981
  • Product Dimensions: 7 x 0.7 x 9 inches
  • Shipping Weight: 1.8 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (17 customer reviews)
  • Amazon Best Sellers Rank: #232,332 in Books (See Top 100 in Books)

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

About the Author

Richard S. Sutton is Senior Research Scientist, Department of Computer Science, University of Massachusetts. Andrew G. Barto is Professor of Computer Science at the University of Massachusetts.

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Customer Reviews

I highly recommend this book to anyone who wants to learn about this subject.
David Tan
In the last chapter of the book the authors overview some of the more successful applications of reinforcement learning, one of them already mentioned.
Dr. Lee D. Carlson
The book provides numerous step-by-step algorithms that makes it relatively easy to get started writing algorithms.
Warren B. Powell

Most Helpful Customer Reviews

26 of 28 people found the following review helpful By David Tan on February 29, 2000
Format: Hardcover
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.
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16 of 17 people found the following review helpful By Kausik Ghatak on February 8, 2009
Format: Hardcover
I am a software developer and worked on applying Reinforcement Learning (RL) in cognitive fields for my patent work (pending).

This book is highly regarded in RL literature and is probably one of the few hand counted books that explicitly address RL as a subject. The book has good balance between subject matter and theory which makes it unique.

However this book has many serious drawbacks. Had there been excellent books on this subject I would have discouraged you to have this one. Rather my advice would be referring to the book "Approximate Dynamic Programming" by "Warren B. Powell" as well. This DP book has formalized the terms used in Sutton's book. This might save you from the ambiguous terminologies used in Sutton's book.

You might like to refer to these two excellent & precise works by Abhijjit Gosavi: //web.mst.edu/ ~gosavia/ tutorial.pdf and //web.mst.edu/ ~gosavia/ joc.pdf.
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15 of 16 people found the following review helpful By Mihailo Despotovic on August 3, 2003
Format: Hardcover
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.
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14 of 15 people found the following review helpful By Warren B. Powell on December 15, 2007
Format: Hardcover
Reinforcement Learning is an exceptionally clear introduction to a field that also goes under names such as approximate dynamic programming, adaptive dynamic programming and neuro-dynamic programming. The book is written entirely from the perspective of computer science, where problems tend to have discrete states (although potentially large state spaces) and (typically) small action spaces.

The book provides numerous step-by-step algorithms that makes it relatively easy to get started writing algorithms. The presentation uses minimal mathematics, and avoids the difficult theory supporting the convergence proofs, making it a nice introduction for undergraduates and graduates alike. But throughout the presentation is evidence of extensive experience with applying these methods to a range of classical problems in artificial intelligence.

Students interested in a stronger theoretical foundation should look at Neuro-Dynamic Programming (Optimization and Neural Computation Series, 3). My recent book, Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics), puts far more emphasis on mathematical modeling, and presents the field more from the perspective of the operations research community. For an edited volume with a number of contributions from both artificial intelligence and control theory, see Handbook of Learning and Approximate Dynamic Programming (IEEE Press Series on Computational Intelligence).

Warren Powell
Professor
Operations Research and Financial Engineering
Princeton University
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