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

We consider a single-server queue in discrete time, in which customers must be served before some limit sojourn time of geometrical distribution. We establish the structural properties of the stochastic dynamic programming operator and we deduce that the optimal policy is of threshold type. Downloads Handbook of Markov Decision Processes : Methods andMarkov decision processes: discrete stochastic dynamic programming. Markov decision processes: discrete stochastic dynamic programming : PDF eBook Download. 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. Puterman Publisher: Wiley-Interscience. A customer who is not served before this limit We use a Markov decision process with infinite horizon and discounted cost. An MDP is a model of a dynamic system whose behavior varies with time. May 9th, 2013 reviewer Leave a comment Go to comments. Handbook of Markov Decision Processes : Methods and Applications . Is a discrete-time Markov process. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2): 257-286.. 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 . A wide variety of stochastic control problems can be posed as Markov decision processes. 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. Markov Decision Processes: Discrete Stochastic Dynamic Programming. However, determining an optimal control policy is intractable in many cases.