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

However, determining an optimal control policy is intractable in many cases. White: 9780471936275: Amazon.com. We base our model on the distinction between the decision .. 394、 Puterman(2005), Markov Decision Processes: Discrete Stochastic Dynamic Programming. Tags:Markov decision processes: Discrete stochastic dynamic programming, tutorials, pdf, djvu, chm, epub, ebook, book, torrent, downloads, rapidshare, filesonic, hotfile, fileserve. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. 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. 395、 Ramanathan(1993), Statistical Methods in Econometrics. 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.