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

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. 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. This book contains information obtained from authentic and highly regarded sources. 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. 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.. Markov Decision Processes: Discrete Stochastic Dynamic Programming. A Survey of Applications of Markov Decision Processes. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. An MDP is a model of a dynamic system whose behavior varies with time. White: 9780471936275: Amazon.com. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005.