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

We base our model on the distinction between the decision .. 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. Markov Decision Processes: Discrete Stochastic Dynamic Programming. This book contains information obtained from authentic and highly regarded sources. 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. Markov Decision Processes: Discrete Stochastic Dynamic Programming . Is a discrete-time Markov process. We consider a single-server queue in discrete time, in which customers must be served before some limit sojourn time of geometrical distribution. 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. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. ETH - Morbidelli Group - Resources Dynamic probabilistic systems. A Survey of Applications of Markov Decision Processes. White: 9780471936275: Amazon.com. 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 . The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. A customer who is not served before this limit We use a Markov decision process with infinite horizon and discounted cost. The second, semi-Markov and decision processes. We establish the structural properties of the stochastic dynamic programming operator and we deduce that the optimal policy is of threshold type. 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.