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

Markov decision processes: discrete stochastic dynamic programming






Markov decision processes: discrete stochastic dynamic programming Martin L. Puterman ebook
Page: 666
Format: pdf
Publisher: Wiley-Interscience
ISBN: 0471619779, 9780471619772


An MDP is a model of a dynamic system whose behavior varies with time. 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. 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. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. A wide variety of stochastic control problems can be posed as Markov decision processes. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics). L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. However, determining an optimal control policy is intractable in many cases. Tags:Markov decision processes: Discrete stochastic dynamic programming, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. This book contains information obtained from authentic and highly regarded sources. Iterative Dynamic Programming | maligivvlPage Count: 332. 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.

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