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

• Markov decision processes: discrete stochastic dynamic programming
• Martin L. Puterman
• Page: 666
• Format: pdf, ePub, mobi, fb2
• 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>

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,. A Linear Programming Approach to Nonstationary Infinite-Horizon
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Martin L. Puterman (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming Wiley. Larry Epstein and Stan Zin (1989)  Optimization of threshold memberships over fuzzy decision process
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