Reinforcement Learning: An Introduction

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MIT Press, 1998 - Computers - 322 pages
14 Reviews

Reinforcement learning, one of the most active research areas in artificialintelligence, is a computational approach to learning whereby an agent tries to maximize the totalamount of reward it receives when interacting with a complex, uncertain environment. InReinforcement Learning, Richard Sutton and Andrew Barto provide a clear andsimple account of the key ideas and algorithms of reinforcement learning. Their discussion rangesfrom the history of the field's intellectual foundations to the most recent developments andapplications. The only necessary mathematical background is familiarity with elementary concepts ofprobability.

The book is divided into three parts. Part I defines thereinforcement learning problem in terms of Markov decision processes. Part II provides basicsolution 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 thefuture of reinforcement learning.

  

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Review: Reinforcement Learning: An Introduction

User Review  - Rami alaa - Goodreads

And I read it again actually I'm reading the HTML version Read full review

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That's where you want to start reading about RL. Read full review

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Contents

Introduction
3
Evaluative Feedback
25
Elementary Solution Methods
87
A Unified View
161
Generalization and Function Approximation
193
Planning and Learning
227
Dimensions of Reinforcement Learning
255
Case Studies
261
References
291
Summary of Notation
313
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Page 301 - Proceedings of the Second International Conference on Simulation of Adaptive Behavior: From Animals to Animals, 2, 460-468.

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Sutton & Barto Book: Reinforcement Learning: An Introduction
Reinforcement Learning: An Introduction. Richard S. Sutton and Andrew G. Barto · MIT Press, Cambridge, MA, 1998 A Bradford Book ...
www.cs.ualberta.ca/ ~sutton/ book/ the-book.html

Trends in Cognitive Sciences : Reinforcement Learning: An ...
Reinforcement Learning: An Introduction, by Sutton, rs and Barto, ag. P. Read Montague Corresponding Author Contact Information ...
linkinghub.elsevier.com/ retrieve/ pii/ S1364661399013315

Reinforcement Learning: An Introduction - Neural Networks, IEEE ...
Reinforcement Learning: An Introduction—Richard S. Sutton. and Andrew G. Barto. (Cambridge, MA: MIT Press, 1998, 340 pp.,. hard cover, $40.00. ...
ieeexplore.ieee.org/ iel4/ 72/ 15435/ 00712192.pdf?arnumber=712192

Richard S. Sutton and Andrew G. Barto (1998) Reinforcement ...
Reinforcement Learning: An Introduction. Chapter 6:. Temporal-Difference Learning. Paul Wagner. T-61.6020 Reinforcement Learning - Theory and Applications ...
www.cis.hut.fi/ Opinnot/ T-61.6020/ 2006/ presentations/ presentation5

citeulike: Reinforcement Learning: An Introduction (Adaptive ...
TY - BOOK ID - citeulike:112017 TI - Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning) PB - {The MIT Press} SN ...
www.citeulike.org/ user/ anoopsarkar/ article/ 112017

Reinforcement Learning: An Introduction
@Book{sutton98a, author = {Richard S. Sutton and Andrew G. Barto}, title = {Reinforcement Learning: An Introduction}, publisher = {{MIT} Press}, ...
jmvidal.cse.sc.edu/ lib/ sutton98a.html

anw.cs.umass.edu Sutton & Barto Book: Reinforcement Learning: An ...
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Reinforcement Learning I: Introduction - Sutton, Barto (researchindex)
Reinforcement learning: An introduction. Cambridge, MA: MIT Press. http://citeseer.ist.psu.edu/sutton98reinforcement.html More ...
citeseer.ist.psu.edu/ sutton98reinforcement.html

The Online Books Page: Reinforcement Learning: An Introduction, by ...
Title: Reinforcement Learning: An Introduction · Author: Sutton, Richard S. Author: Barton, Andrew G. Note: electronic edition. Link: HTML at umass ...
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ingentaconnect Reinforcement Learning: An Introduction - rs Sutton ...
Reinforcement Learning: An Introduction - rs Sutton, ag Barto, MIT Press, Cambridge, MA 1998, 322 pp. ISBN 0-262-19398-1. Authors: Johnson jd; Li J.; ...
www.ingentaconnect.com/ content/ els/ 09252312/ 2000/ 00000035/ 00000001/ art00324;jsessionid=dgjcjlrkn0ci.alexandra?format...

About the author (1998)

JENNIE SI is Professor of Electrical Engineering, Arizona State University, Tempe, AZ. She is director of Intelligent Systems Laboratory, which focuses on analysis and design of learning and adaptive systems. In addition to her own publications, she is the Associate Editor for IEEE Transactions on Neural Networks, and past Associate Editor for IEEE Transactions on Automatic Control and IEEE Transactions on Semiconductor Manufacturing. She was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming.

ANDREW G. BARTO is Professor of Computer Science, University of Massachusetts, Amherst. He is co-director of the Autonomous Learning Laboratory, which carries out interdisciplinary research on machine learning and modeling of biological learning. He is a core faculty member of the Neuroscience and Behavior Program of the University of Massachusetts and was the co-chair for the 2002 NSF Workshop on Learning and Approximate Dynamic Programming. He currently serves as an associate editor of Neural Computation.

WARREN B. POWELL is Professor of Operations Research and Financial Engineering at Princeton University. He is director of CASTLE Laboratory, which focuses on real-time optimization of complex dynamic systems arising in transportation and logistics.

DONALD C. WUNSCH is the Mary K. Finley Missouri Distinguished Professor in the Electrical and Computer Engineering Department at the University of Missouri, Rolla. He heads the Applied Computational Intelligence Laboratory and also has a joint appointment in Computer Science, and is President-Elect of the International Neural Networks Society.

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