Markov Decision Processes with Their Applications by Qiying Hu (English) Hardcov

Description: Markov Decision Processes with Their Applications by Qiying Hu, Wuyi Yue Examines Markov Decision Processes - also called stochastic dynamic programming - and their applications in the optimal control of discrete event systems, optimal replacement, and optimal allocations in sequential online auctions. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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. Notes 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. 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. Table of Contents Discretetimemarkovdecisionprocesses: Total Reward.- Discretetimemarkovdecisionprocesses: Average Criterion.- Continuous Time Markov Decision Processes.- Semi-Markov Decision Processes.- Markovdecisionprocessesinsemi-Markov Environments.- Optimal control of discrete event systems: I.- Optimal control of discrete event systems: II.- Optimal replacement under stochastic Environments.- Optimalal location in sequential online Auctions. Review From the reviews:"Markov decision processes (MDPs) are one of the most comprehensively investigated branches in mathematics. … Very beneficial also are the notes and references at the end of each chapter. … we can recommend the book … for readers who are familiar with Markov decision theory and who are interested in a new approach to modelling, investigating and solving complex stochastic dynamic decision problems." (Peter Köchel, Mathematical Reviews, Issue 2009 c) Long Description 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. Review Quote From the reviews:"Markov decision processes (MDPs) are one of the most comprehensively investigated branches in mathematics. … Very beneficial also are the notes and references at the end of each chapter. … we can recommend the book … for readers who are familiar with Markov decision theory and who are interested in a new approach to modelling, investigating and solving complex stochastic dynamic decision problems." (Peter Köchel, Mathematical Reviews, Issue 2009 c) Feature Presents new branches for Markov Decision Processes (MDP) Applies new methodology for MDPs with discounted total reward criterion Offers new applications of MDPs in areas such as the control of discrete event systems and the optimal allocations in sequential online auctions Shows the validity of the optimality equation and its properties from the definition of the model by reducing the scale of MDP models based on action reduction and state decomposition Presents two new optimal control problems for discrete event systems Examines two optimal replacement problems in stochastic environments Studies continuous time MDPs and semi-Markov decision processes in a semi-Markov environment Description for Sales People 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. Details ISBN0387369503 Author Wuyi Yue Short Title MARKOV DECISION PROCESSES W/TH Pages 297 Series Advances in Mechanics and Mathematics Language English ISBN-10 0387369503 ISBN-13 9780387369501 Media Book Format Hardcover Series Number 14 Year 2007 Imprint Springer-Verlag New York Inc. Place of Publication New York, NY Country of Publication United States DEWEY 519.233 Affiliation Konan University, Kobe, Japan DOI 10.1604/9780387369501;10.1007/978-0-387-36951-8 AU Release Date 2007-11-26 NZ Release Date 2007-11-26 US Release Date 2007-11-26 UK Release Date 2007-11-26 Publisher Springer-Verlag New York Inc. Publication Date 2007-11-26 Alternative 9781441942388 Audience Undergraduate Illustrations XV, 297 p. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96272797;

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