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

An MDP is a model of a dynamic system whose behavior varies with time. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. However, determining an optimal control policy is intractable in many cases. 395、 Ramanathan(1993), Statistical Methods in Econometrics. 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. This book contains information obtained from authentic and highly regarded sources. Iterative Dynamic Programming | maligivvlPage Count: 332. 394、 Puterman(2005), 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. We base our model on the distinction between the decision .. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. White: 9780471936275: Amazon.com. A wide variety of stochastic control problems can be posed as Markov decision processes.