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

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 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. A wide variety of stochastic control problems can be posed as Markov decision processes. 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). However, determining an optimal control policy is intractable in many cases. €�If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. Markov Decision Processes: Discrete Stochastic Dynamic Programming. 32 books cite this book: Markov Decision Processes: Discrete Stochastic Dynamic Programming. White: 9780471936275: Amazon.com. Markov Decision Processes: Discrete Stochastic Dynamic Programming (Wiley Series in Probability and Statistics). Of the Markov Decision Process (MDP) toolbox V3 (MATLAB).