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Markov Decision Processes with Their Applications

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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.

Author(s): Qiying Hu, Wuyi Yue
Series: Advances in Mechanics and Mathematics 14
Edition: 1
Publisher: Springer US
Year: 2008

Language: English
Pages: 297
City: New York
Tags: Operations Research, Mathematical Programming; Probability Theory and Stochastic Processes; Calculus of Variations and Optimal Control; Optimization; Industrial and Production Engineering

Front Matter....Pages i-xv
Introduction....Pages 1-9
Discretetimemarkovdecisionprocesses: Total Reward....Pages 11-38
Discretetimemarkovdecisionprocesses: Average Criterion....Pages 39-61
Continuous Time Markov Decision Processes....Pages 63-103
Semi-Markov Decision Processes....Pages 105-120
Markovdecisionprocessesinsemi-Markov Environments....Pages 121-175
Optimal control of discrete event systems: I....Pages 177-202
Optimal control of discrete event systems: II....Pages 203-231
Optimal replacement under stochastic Environments....Pages 233-263
Optimalal location in sequential online Auctions....Pages 265-286
Back Matter....Pages 287-297