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

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. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2): 257-286.. Handbook of Markov Decision Processes : Methods and Applications . Markov Decision Processes: Discrete Stochastic Dynamic Programming . The second, semi-Markov and decision processes. An MDP is a model of a dynamic system whose behavior varies with time. Downloads Handbook of Markov Decision Processes : Methods andMarkov decision processes: discrete stochastic dynamic programming. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. A Survey of Applications of Markov Decision Processes. ETH - Morbidelli Group - Resources Dynamic probabilistic systems.