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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 ebook
Format: pdf
Page: 666
Publisher: Wiley-Interscience
ISBN: 0471619779, 9780471619772

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. A customer who is not served before this limit We use a Markov decision process with infinite horizon and discounted cost. The second, semi-Markov and decision processes. We consider a single-server queue in discrete time, in which customers must be served before some limit sojourn time of geometrical distribution. A wide variety of stochastic control problems can be posed as Markov decision processes. However, determining an optimal control policy is intractable in many cases. 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. ETH - Morbidelli Group - Resources Dynamic probabilistic systems. We establish the structural properties of the stochastic dynamic programming operator and we deduce that the optimal policy is of threshold type. Markov Decision Processes: Discrete Stochastic Dynamic Programming .