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

This book contains information obtained from authentic and highly regarded sources. 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. With the development of science and technology, there are large numbers of complicated and stochastic systems in many areas, including communication (Internet and wireless), manufacturing, intelligent robotics, and traffic management etc.. 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. Iterative Dynamic Programming | maligivvlPage Count: 332. A wide variety of stochastic control problems can be posed as Markov decision processes. Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. 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 .. 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. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics).