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Markov decision processes: discrete stochastic dynamic programming Martin L. Puterman
Publisher: Wiley-Interscience

A wide variety of stochastic control problems can be posed as Markov decision processes. Markov Decision Processes: Discrete Stochastic Dynamic Programming . Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. E-book Markov decision processes: Discrete stochastic dynamic programming online. This book presents a unified theory of dynamic programming and Markov decision processes and its application to a major field of operations research and operations management: inventory control. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. The second, semi-Markov and decision processes. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Downloads Handbook of Markov Decision Processes : Methods andMarkov decision processes: discrete stochastic dynamic programming. MDPs can be used to model and solve dynamic decision-making Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. Models are developed in discrete time as For these models, however, it seeks to be as comprehensive as possible, although finite horizon models in discrete time are not developed, since they are largely described in existing literature. ETH - Morbidelli Group - Resources Dynamic probabilistic systems. Handbook of Markov Decision Processes : Methods and Applications . Proceedings of the IEEE, 77(2): 257-286.. A tutorial on hidden Markov models and selected applications in speech recognition. However, determining an optimal control policy is intractable in many cases.