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

White: 9780471936275: Amazon.com. An MDP is a model of a dynamic system whose behavior varies with time. 395、 Ramanathan(1993), Statistical Methods in Econometrics. Markov Decision Processes: Discrete Stochastic Dynamic Programming. A tutorial on hidden Markov models and selected applications in speech recognition. Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. 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. 394、 Puterman(2005), Markov Decision Processes: Discrete Stochastic Dynamic Programming. A Survey of Applications of Markov Decision Processes. 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. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. Proceedings of the IEEE, 77(2): 257-286.. E-book Markov decision processes: Discrete stochastic dynamic programming online. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. 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. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. The elements of an MDP model are the following [7]:(1)system states,(2)possible actions at each system state,(3)a reward or cost associated with each possible state-action pair,(4)next state transition probabilities for each possible state-action pair.