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Markov decision processes: discrete stochastic dynamic programming. Martin L. Puterman

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ISBN: 9780471619772 | 666 pages | 17 Mb

• Markov decision processes: discrete stochastic dynamic programming
• Martin L. Puterman
• Page: 666
• Format: pdf, ePub, fb2, mobi
• ISBN: 9780471619772
• Publisher: Wiley-Interscience

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<p>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.</p> <p> From the Publisher</p> <p> 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. </p>

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Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics) By Martin L. Puterman Risk-Aware Decision Making and Dynamic Programming
The present paper focuses on the dynamic programming part. It discusses .. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley  Markov decision process
Martin Puterman, Markov decision processes, John Wiley & Sons, 1994. D.P. Bertsekas, Dynamic Programming, Prentice Hall, 1987. Xiaolan Xie . a discrete- time stochastic dynamic system, with; costs generated over time. State t. State t+1 . Markov decision processes with delays and asynchronous cost
Abstract—Markov decision processes (MDPs) may involve three types of delays. First, state the finite state space of a discrete-time Markov chain that de- scribes a system the . dynamic programming methods used in operations research. A - value [18] is a . We now discuss stochastic delayed MDPs (SDMDPs). First,. Solving Very Large Weakly Coupled Markov Decision Processes
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