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

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). A Survey of Applications of Markov Decision Processes. €If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. €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. Markov decision processes: discrete stochastic dynamic programming : PDF eBook Download. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Of the Markov Decision Process (MDP) toolbox V3 (MATLAB). 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. Puterman Publisher: Wiley-Interscience. May 9th, 2013 reviewer Leave a comment Go to comments.