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

Is a discrete-time Markov process. 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. 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 . Iterative Dynamic Programming | maligivvlPage Count: 332. Markov Decision Processes: Discrete Stochastic Dynamic Programming. A tutorial on hidden Markov models and selected applications in speech recognition. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics). Proceedings of the IEEE, 77(2): 257-286.. 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). 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. 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.. This book contains information obtained from authentic and highly regarded sources.