Markov Decision Processes with Their Applications

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.

Sommario

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.

Riassunto

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.

Testo aggiuntivo

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)

Relazione

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)

Dettagli sul prodotto

• Autori: Qiyin Hu, Qiying Hu, Wuyi Yue
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 14.04.2009
• EAN: 9780387369501
• ISBN: 978-0-387-36950-1
• Pagine: 297
• Dimensioni: 168 mm x 22 mm x 243 mm
• Peso: 637 g
• Illustrazioni: XV, 297 p.
• Serie: Advances in Mechanics and Mathematics; Advances in Mechanics and Mathematics 78320; Advances in Mechanics and Mathematics
• Categoria: Scienze naturali, medicina, informatica, tecnica > Matematica > Altro