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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning Series) Hardcover – 8 May 1998

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Product details

  • Hardcover: 344 pages
  • Publisher: MIT Press (8 May 1998)
  • Language: English
  • ISBN-10: 9780262193986
  • ISBN-13: 978-0262193986
  • ASIN: 0262193981
  • Product Dimensions: 17.8 x 2.1 x 22.9 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (5 customer reviews)
  • Amazon Bestsellers Rank: 270,144 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

From the Author

A unified approach to AI, machine learning, and control

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 this book, we provide an explanation of the key ideas and algorithms of reinforcement learning. The 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. This book is meant to be an introductory treatment of reinforcement learning, emphasizing foundations and ideas rather than the latest developments and mathematical proofs. We divide the ideas underlying the field into a half dozen primary dimensions, consider each in detail, and then combine them to form a much larger space of possible methods including all the most popular ones from Q-learning to value iteration and heuristic search. In this way we have tried to make the book interesting to both newcomers and experts alike. We have tried to make the work accessible to the broadest possible audiences in artificial intelligence, control engineering, operations research, psychology, and neuroscience. If you are a teacher, we urge you to consider creating or altering a course to use the book. We have found that the book works very well as the text for a course on reinforcement learning at the graduate or advanced undergraduate level. The eleven chapters can be covered one per week. Exercises are provided in each chapter to help the students think on their own about the material. Answers to the exercises are available to instructors, for now from me, and probably later from MIT Press in an instructor's manual. Programming projects are also suggested throughout the book. Of course, the book can also be used to help teach reinforcement learning as it is most commonly done now, that is, as part of a broader course on machine learning, artificial intelligence, neural networks, or advanced control. I have taught all the material in the book in as little as four weeks, and of course subsets can be covered in less time. Table of contents: Part I: The Problem 1 Introduction 2 Evaluative Feedback 3 The Reinforcement Learning Problem Part II: Elementary Methods 4 Dynamic Programming 5 Monte Carlo Methods 6 Temporal Difference Learning Part III: A Unified View 7 Eligibility Traces 8 Generalization and Function Approximation 9 Planning and Learning 10 Dimensions of Rreinforcement Learning 11 Case Studies For further information, see http://envy.cs.umass.edu/~rich/book/the-book.html.

About the Author

Andrew G. Barto is Professor of Computer Science at the University of Massachusetts.


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Format: Hardcover Verified Purchase
Not that there are many books on Reinforcement Learning, but this is probably the best there is. Very easy to read, covers all basic material (and some more advanced) it is actually a very enjoyable book to read if you are in the field of A.I. or robotics. Well written, with many examples and a few graphs, and explained mathematical formulas.
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Format: Hardcover
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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By A Customer on 20 Oct. 1998
Format: Hardcover
If you are interested in AI or more specifically, RL, I recommend buying and reading this book! It is well structured, concise, and complete. I feel like I have a strong background in Reinforcement Learning after reading this book and working some of the example problems.
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By S.Coda on 20 Feb. 2014
Format: Kindle Edition Verified Purchase
This book covers the ground essential to understanding much of the work out their published on RL. It's hard going but worth the effort, if you can stand the relentless bellman equations. I found the monte-carlo sections of this book particularly grueling, but that I think says more about my limits than the content of this book.
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By DLP on 6 May 2014
Format: Hardcover Verified Purchase
This is a great book on reinforcement learning. There is enough detail for the reader to be able to implement a decent reinforcement learning algorithm after reading this book.
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