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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,...

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Main Authors: Hu, Qiying. (Author, http://id.loc.gov/vocabulary/relators/aut), Yue, Wuyi. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: New York, NY : Springer US : Imprint: Springer, 2008.
Edition:1st ed. 2008.
Series:Advances in Mechanics and Mathematics, 14
Subjects:
Operations research.
Management science.
Probabilities.
Calculus of variations.
Industrial engineering.
Production engineering.
Operations Research, Management Science.
Probability Theory and Stochastic Processes.
Calculus of Variations and Optimal Control; Optimization.
Industrial and Production Engineering.
Online Access:https://doi.org/10.1007/978-0-387-36951-8
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.

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