Markov Decision Processes: Discrete Stochastic Dynamic Programming

by Martin L. Puterman

Overview

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential."
—Zentralblatt fur Mathematik

". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes."
—Journal of the American Statistical Association

Product Details

ISBN-13:	9781118625873
Publisher:	Wiley
Publication date:	08/28/2014
Series:	Wiley Series in Probability and Statistics
Sold by:	JOHN WILEY & SONS
Format:	eBook
Pages:	684
File size:	23 MB
Note:	This product may take a few minutes to download.

About the Author

Martin L. Puterman, PhD, is Advisory Board Professor of Operations and Director of the Centre for Operations Excellence at The University of British Columbia in Vancouver, Canada.

Table of Contents

Preface.

1. Introduction.

2. Model Formulation.

3. Examples.

4. Finite-Horizon Markov Decision Processes.

5. Infinite-Horizon Models: Foundations.

6. Discounted Markov Decision Problems.

7. The Expected Total-Reward. Criterion.

8. Average Reward and Related Criteria.

9. The Average Reward Criterion-Multichain and Communicating Models.

10. Sensitive Discount Optimality.

11. Continuous-Time Models.

Afterword.

Notation.

Appendix A. Markov Chains.

Appendix B. Semicontinuous Functions.

Appendix C. Normed Linear Spaces.

Appendix D. Linear Programming.

Bibliography.

Index.

Editorial Reviews

An up-to-date, unified, and rigorous treatment of the theoretical, computational, and applied research on Markov decision process models. It discusses all major research directions in the field, highlights many significant applications, and explores a number of important topics that have either been ignored or given short shrift in the literature. The focus is primarily on infinite horizon discrete time models and models with discrete time spaces, but models with arbitrary state spaces, finite horizon models, and continuous-time discrete state models are also examined. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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