Markov decision processes : discrete stochastic dynamic programming / Martin L. Puterman.

Format:

Book

Author/Creator:

Puterman, Martin L.

Contributor:

Wiley InterScience (Online service)

Lippincott Library Book Endowment Fund.

Series:

Wiley series in probability and mathematical statistics. Applied probability and statistics

Wiley series in probability and mathematical statistics. Applied probability and statistics section

Language:

English

Subjects (All):

Markov processes.

Statistical decision.

Dynamic programming.

Stochastic processes.

Linear programming.

Markov Chains.

Stochastic Processes.

Programming, Linear.

Medical Subjects:

Markov Chains.

Stochastic Processes.

Programming, Linear.

Physical Description:

1 online resource (xvii, 649 pages) : illustrations.

polychrome

Place of Publication:

New York : Wiley, [1994]

System Details:

text file

Summary:

The past decade has seen considerable theoretical and applied research on Markov decision processes, as well as the growing use of these models in ecology, economics, communications engineering, and other fields where outcomes are uncertain and sequential decision-making processes are needed. A timely response to this increased activity, Martin L. Puterman's new work provides a uniquely 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 of Markov decision processes models, and explores numerous important topics that have previously been neglected or given cursory coverage in the literature. Markov Decision Processes focuses primarily on infinite horizon discrete time models and models with discrete time spaces while also examining models with arbitrary state spaces, finite horizon models, and continuous-time discrete state models. The book is organized around optimality criteria, using a common framework centered on the optimality (Bellman) equation for presenting results. The results are presented in a "theorem-proof" format and elaborated on through both discussion and examples, including results that are not available in any other book. A two-state Markov decision process model, presented in Chapter 3, is analyzed repeatedly throughout the book and demonstrates many results and algorithms. Markov Decision Processes covers recent research advances in such areas as countable state space models with average reward criterion, constrained models, and models with risk sensitive optimality criteria. It alsoexplores several topics that have received little or no attention in other books, including modified policy iteration, multichain models with average reward criterion, and sensitive optimality. In addition, a Bibliographic Remarks section in each chapter comments on relevant historical references in the book's extensive, up-to-date bibliography...numerous figures illustrate examples, algorithms, results, and computations...a biographical sketch highlights the life and work of A. A. Markov...an afterword discusses partially observed models and other key topics...and appendices examine Markov chains, normed linear spaces, semi-continuous functions, and linear programming. Markov Decision Processes will prove to be invaluable to researchers in operations research, management science, and control theory. Its applied emphasis will serve the needs of researchers in communications and control engineering, economics, statistics, mathematics, computer science, and mathematical ecology. Moreover, its conceptual development from simple to complex models, numerous applications in text and problems, and background coverage of relevant mathematics will make it a highly useful textbook in courses on dynamic programming and stochastic control.

Contents:

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

Appendix A. Markov Chains

Appendix B. Semicontinuous Functions

Appendix C. Normed Linear Spaces

Appendix D. Linear Programming.

Notes:

Includes bibliographical references (pages 613-642) and index.

Electronic reproduction. Hoboken, N.J. Available via World Wide Web.

Print version record.

Local Notes:

Acquired for the Penn Libraries with assistance from the Lippincott Library Book Endowment Fund.

Other Format:

Print version: Puterman, Martin L. Markov decision processes.

ISBN:

9780470316887

0470316888

9780470317723

0470317728

0471619779

9780471619772

Publisher Number:

99990218752

Access Restriction:

Restricted for use by site license.