Examples in Markov Decision Processes

Examples in Markov Decision Processes
Author :
Publisher : World Scientific
Total Pages : 308
Release :
ISBN-10 : 9781848167940
ISBN-13 : 1848167946
Rating : 4/5 (946 Downloads)

Book Synopsis Examples in Markov Decision Processes by : A. B. Piunovskiy

Download or read book Examples in Markov Decision Processes written by A. B. Piunovskiy and published by World Scientific. This book was released on 2012 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the theory to real-life problems like stock exchange, queues, gambling, optimal search etc, the main attention is paid to counter-intuitive, unexpected properties of optimization problems. Such examples illustrate the importance of conditions imposed in the theorems on Markov Decision Processes. Many of the examples are based upon examples published earlier in journal articles or textbooks while several other examples are new. The aim was to collect them together in one reference book which should be considered as a complement to existing monographs on Markov decision processes.The book is self-contained and unified in presentation.The main theoretical statements and constructions are provided, and particular examples can be read independently of others. Examples in Markov Decision Processes is an essential source of reference for mathematicians and all those who apply the optimal control theory to practical purposes. When studying or using mathematical methods, the researcher must understand what can happen if some of the conditions imposed in rigorous theorems are not satisfied. Many examples confirming the importance of such conditions were published in different journal articles which are often difficult to find. This book brings together examples based upon such sources, along with several new ones. In addition, it indicates the areas where Markov decision processes can be used. Active researchers can refer to this book on applicability of mathematical methods and theorems. It is also suitable reading for graduate and research students where they will better understand the theory.


Examples in Markov Decision Processes Related Books

Examples in Markov Decision Processes
Language: en
Pages: 308
Authors: A. B. Piunovskiy
Categories: Mathematics
Type: BOOK - Published: 2012 - Publisher: World Scientific

GET EBOOK

This invaluable book provides approximately eighty examples illustrating the theory of controlled discrete-time Markov processes. Except for applications of the
Markov Decision Processes
Language: en
Pages: 544
Authors: Martin L. Puterman
Categories: Mathematics
Type: BOOK - Published: 2014-08-28 - Publisher: John Wiley & Sons

GET EBOOK

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 g
Markov Decision Processes with Applications to Finance
Language: en
Pages: 393
Authors: Nicole Bäuerle
Categories: Mathematics
Type: BOOK - Published: 2011-06-06 - Publisher: Springer Science & Business Media

GET EBOOK

The theory of Markov decision processes focuses on controlled Markov chains in discrete time. The authors establish the theory for general state and action spac
Markov Chains and Decision Processes for Engineers and Managers
Language: en
Pages: 478
Authors: Theodore J. Sheskin
Categories: Mathematics
Type: BOOK - Published: 2016-04-19 - Publisher: CRC Press

GET EBOOK

Recognized as a powerful tool for dealing with uncertainty, Markov modeling can enhance your ability to analyze complex production and service systems. However,
Decision Making Under Uncertainty
Language: en
Pages: 350
Authors: Mykel J. Kochenderfer
Categories: Computers
Type: BOOK - Published: 2015-07-24 - Publisher: MIT Press

GET EBOOK

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to