Markov Decision Processes with Their Applications

Qiying Hu|Wuyi Yue

ISBN 10: 1441942386 / ISBN 13: 9781441942388

Published by Springer US, 2010

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents new branches for Markov Decision Processes (MDP)Applies new methodology for MDPs with discounted total reward criterionOffers new applications of MDPs in areas such as the control of discrete event systems and the optimal allocatio.

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Bibliographic Details

• Title: Markov Decision Processes with Their Applications
• Publisher: Springer US
• Publication Date: 2010
• Language: English
• ISBN 10: 1441942386
• ISBN 13: 9781441942388
• Binding: Soft cover
• Condition: New

About this title

Synopsis

MDPs have been applied in many areas, such as communications, signal processing, artificial intelligence, stochastic scheduling and manufacturing systems, discrete event systems, management and economies. This book examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents three main topics: a new methodology for MDPs with discounted total reward criterion; transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs; application of MDPs in stochastic environments, which greatly extends the area where MDPs can be applied. Each topic is used to study optimal control problems or other types of problems.

From the Back Cover

Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters.

Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. The book presents four main topics that are used to study optimal control problems:

*a new methodology for MDPs with discounted total reward criterion;

*transformation of continuous-time MDPs and semi-Markov decision processes into a discrete-time MDPs model, thereby simplifying the application of MDPs;

*MDPs in stochastic environments, which greatly extends the area where MDPs can be applied;

*applications of MDPs in optimal control of discrete event systems, optimal replacement, and optimal allocation in sequential online auctions.

This book is intended for researchers, mathematicians, advanced graduate students, and engineers who are interested in optimal control, operation research, communications, manufacturing, economics, and electronic commerce.