Markov Decision Processes With Their Applications

• Main Authors: Hu, Qiying (Author), Yue, Wuyi (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Boston, MA : Springer US, 2008.
• Series: Advances in Mechanics and Mathematics ; 14
• Subjects: Mathematics.; Calculus of variations.; Operations research.; Management science.; Probabilities.; Industrial engineering.; Production engineering.; Operations Research, Management Science.; Probability Theory and Stochastic Processes.; Calculus of Variations and Optimal Control; Optimization.; Industrial and Production Engineering.

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,...

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.