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

A tutorial on hidden Markov models and selected applications in speech recognition. €�If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. 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. 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). Proceedings of the IEEE, 77(2): 257-286.. Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. 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. 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. 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. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics). An MDP is a model of a dynamic system whose behavior varies with time. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Of the Markov Decision Process (MDP) toolbox V3 (MATLAB). Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005. €�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. 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.