Markov decision processes: discrete stochastic dynamic programming. Martin L. Puterman
ISBN: 0471619779,9780471619772 | 666 pages | 17 Mb
Markov decision processes: discrete stochastic dynamic programming Martin L. Puterman
We modeled this problem as a sequential decision process and used stochastic dynamic programming in order to find the optimal decision at each decision stage. �If you are interested in solving optimization problem using stochastic dynamic programming, have a look at this toolbox. However, determining an optimal control policy is intractable in many cases. 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. 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 wide variety of stochastic control problems can be posed as Markov decision processes. The novelty in our approach is to thoroughly blend the stochastic time with a formal approach to the problem, which preserves the Markov property. 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. 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. Of the Markov Decision Process (MDP) toolbox V3 (MATLAB). We base our model on the distinction between the decision ..
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