Markov decision processes: discrete stochastic dynamic programming by Martin L. Puterman
Markov decision processes: discrete stochastic dynamic programming Martin L. Puterman ebook
ISBN: 0471619779, 9780471619772
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. We establish the structural properties of the stochastic dynamic programming operator and we deduce that the optimal policy is of threshold type. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. This book contains information obtained from authentic and highly regarded sources. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Markov decision processes: discrete stochastic dynamic programming : PDF eBook Download. We base our model on the distinction between the decision .. A customer who is not served before this limit We use a Markov decision process with infinite horizon and discounted cost. May 9th, 2013 reviewer Leave a comment Go to comments. 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. White: 9780471936275: Amazon.com. 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. We consider a single-server queue in discrete time, in which customers must be served before some limit sojourn time of geometrical distribution. 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.. Iterative Dynamic Programming | maligivvlPage Count: 332.