Qiying Hu, Wuyi Yue

Markov Decision Processes with Their Applications (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer US
• Seitenzahl: 297
• Erscheinungstermin: 14. September 2007
• Englisch
• ISBN-13: 9780387369518
• Artikelnr.: 37286644

Autorenporträt

Qiying Hu, Fudan University, Shang Hai, China / Wuyi Yue, Konan University, Kobe, Japan

Inhaltsangabe

List of Figures.- List of Tables.- Preface.- Acknowledgments.- 1. Introduction.- 2. Discrete-Time Markov Decision Processes: Total Reward.- 3. Discrete-Time Markov Decision Processes: Average Criterion.- 4. Continuous-Time Markov Decision Processes.- 5. Semi-Markov Decision Processes.- 6. Markov Decision Processes in Semi-Markov Environments.- 7. Optimal Control of Discrete Event Systems: I. 8. Optimal Control of Discrete Event Systems: II.- 9. Optimal Replacement Under Stochastic Environments.- 10. Optimal Allocation in Sequential Online Auctions.- Index.

Rezensionen

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)