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

~ Richard S. Sutton (Author), Andrew G. Barto (Author) "The idea that we learn by interacting with our environment is probably the first to occur to us when we think about the nature of..." (more)
4.3 out of 5 stars  See all reviews (14 customer reviews)
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"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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Richard S. Sutton
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4.3 out of 5 stars (14 customer reviews)
 
 
 
 
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20 of 22 people found the following review helpful:
5.0 out of 5 stars An excellent introduction to Reinforcement Learning, February 29, 2000
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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13 of 14 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".

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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
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.
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Most Recent Customer Reviews

3.0 out of 5 stars || Good Reference- Much of a draft version||
I am a software developer and worked on applying Reinforcement Learning (RL) in cognitive fields for my patent work (pending). Read more
Published 13 months ago by Kausik Ghatak

5.0 out of 5 stars Best intro book into reinforcement learning.
This book was a great help at writing and programming for my thesis. It has a ton of information, and even I was able to understand and follow the math. Read more
Published 13 months ago by William J. Andrus

5.0 out of 5 stars From the author of Approximate Dynamic Programming
Reinforcement Learning is an exceptionally clear introduction to a field that also goes under names such as approximate dynamic programming, adaptive dynamic programming and... Read more
Published on December 15, 2007 by Warren B. Powell

4.0 out of 5 stars Q-learner
I agree with reviewer Frank "Good introduction but not well structured, May 8, 2005" the authors are over-anxious to establish the credentials of RL in older research traditions... Read more
Published on February 19, 2007 by _eam 0 n_

3.0 out of 5 stars Good introduction but not well structured
This book provides an easy to read introduction in reinforcement learning. It covers several approaches (dynamic programming, monte carlo, temproal differnce) and gives a lot of... Read more
Published on May 8, 2005 by Zac

5.0 out of 5 stars An excellent introduction

As a subfield of artificial intelligence, reinforcement learning has shown great success from both a theoretical and practical viewpoint. Read more
Published on November 5, 2004 by Dr. Lee D. Carlson

5.0 out of 5 stars A Standard, Excellent Introductory Book
This book is undoubtedly the standard book on the topic of reinforcement learning by the two leading researchers in this field. Read more
Published on November 30, 2003 by Li

4.0 out of 5 stars Student
This book is easy to read and understand. But.... For those examples, the authors should provide more details about the solution procedures...How to get the chars. Read more
Published on February 4, 2002 by YI-CHI WANG

5.0 out of 5 stars A thorough introduction to the field.
The book covers all of the basic algorithms in Reinforcement Learning. The exposition mixes theoretical justifications for the algorithms with practical examples. Read more
Published on June 15, 1999

1.0 out of 5 stars Need solution
Why are you holding solution manual? I'm a college graduate with degree in ME. Stop torturing us. Share the information, please.
Published on May 6, 1999

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