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

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. The second, semi-Markov and decision processes. Is a discrete-time Markov process. A path-breaking account of Markov decision processes-theory and computation. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. 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. 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 above finite and infinite horizon Markov decision processes fall into the broader class of Markov decision processes that assume perfect state information-in other words, an exact description of the system. LINK: Download Stochastic Dynamic Programming and the C… eBook (PDF). An MDP is a model of a dynamic system whose behavior varies with time. We base our model on the distinction between the decision .. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. Markov Decision Processes: Discrete Stochastic Dynamic Programming . Commonly used method for studying the problem of existence of solutions to the average cost dynamic programming equation (ACOE) is the vanishing-discount method, an asymptotic method based on the solution of the much better . ETH - Morbidelli Group - Resources Dynamic probabilistic systems. 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.