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

Markov Decision Processes: Discrete Stochastic Dynamic Programming. A path-breaking account of Markov decision processes-theory and computation. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. 395、 Ramanathan(1993), Statistical Methods in Econometrics. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. 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. LINK: Download Stochastic Dynamic Programming and the C… eBook (PDF). 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. 394、 Puterman(2005), Markov Decision Processes: Discrete Stochastic Dynamic Programming. However, determining an optimal control policy is intractable in many cases. We modeled this problem as a sequential decision process and used stochastic dynamic programming in order to find the optimal decision at each decision stage. A wide variety of stochastic control problems can be posed as Markov decision processes. A Survey of Applications of Markov Decision Processes. We base our model on the distinction between the decision .. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics).