Reinforcement Learning: An Introduction / Edition 1

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Overview

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. InReinforcement 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. PartIII 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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Editorial Reviews

Mike James
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!
Computer Shopper, November 1998
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Product Details

  • ISBN-13: 9780262193986
  • Publisher: MIT Press
  • Publication date: 3/1/1998
  • Series: Adaptive Computation and Machine Learning series
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 342
  • Sales rank: 766,129
  • Product dimensions: 7.00 (w) x 9.00 (h) x 0.68 (d)

Table of Contents

Series Foreword
Preface
I The Problem 1
1 Introduction 3
2 Evaluative Feedback 25
3 The Reinforcement Learning Problem 51
II Elementary Solution Methods 87
4 Dynamic Programming 89
5 Monte Carlo Methods 111
6 Temporal-Difference Learning 133
III A Unified View 161
7 Eligibility Traces 163
8 Generalization and Function Approximation 193
9 Planning and Learning 227
10 Dimensions of Reinforcement Learning 255
11 Case Studies 261
References 291
Summary of Notation 313
Index 315
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