Markov decision processes: discrete stochastic dynamic programming ebook
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Markov decision processes: discrete stochastic dynamic programming by Martin L. Puterman
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Markov decision processes: discrete stochastic dynamic programming Martin L. Puterman ebook
Format: pdf
Page: 666
Publisher: Wiley-Interscience
ISBN: 0471619779, 9780471619772
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. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. 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 . Dynamic programming (or DP) is a powerful optimization technique that consists of breaking a problem down into smaller sub-problems, where the sub-problems are not independent. Is a discrete-time Markov process. Markov Decision Processes: Discrete Stochastic Dynamic Programming. 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.. 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. 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. I start by focusing on two well-known algorithm examples ( fibonacci sequence and the knapsack problem), and in the next post I will move on to consider an example from economics, in particular, for a discrete time, discrete state Markov decision process (or reinforcement learning).