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Markov decision processes: discrete stochastic dynamic programming

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An up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. Concentrates on infinite-horizon discrete-time models. Discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models. Also covers modified policy iteration, multichain models with average reward criterion and sensitive optimality. Features a wealth of figures which illustrate examples and an extensive bibliography.

Author(s): Martin L. Puterman
Series: Wiley Series in Probability and Statistics
Edition: 1
Publisher: Wiley-Interscience
Year: 1994

Language: English
Pages: 666