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

Proceedings of the IEEE, 77(2): 257-286.. €�If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. A tutorial on hidden Markov models and selected applications in speech recognition. Of the Markov Decision Process (MDP) toolbox V3 (MATLAB). €�The MDP toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: backwards induction, value iteration, policy iteration, linear programming algorithms with some variants. 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 (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. L., Markov Decision Processes: Discrete Stochastic Dynamic Programming, John Wiley and Sons, New York, NY, 1994, 649 pages. MDPs can be used to model and solve dynamic decision-making Markov Decision Processes With Their Applications examines MDPs and their applications in the optimal control of discrete event systems (DESs), optimal replacement, and optimal allocations in sequential online auctions. Markov Decision Processes: Discrete Stochastic Dynamic Programming.