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A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 133 - Number 10 |
| Year of Publication: 2016 |
| Authors: Sanaa Chafik, Cherki Daoui |
10.5120/ijca2016908033
|
Sanaa Chafik, Cherki Daoui . A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes. International Journal of Computer Applications. 133, 10 ( January 2016), 28-33. DOI=10.5120/ijca2016908033
Abstract
The running time of the classical algorithms of the Markov Decision Process (MDP) typically grows linearly with the state space size, which makes them frequently intractable. This paper presents a Modified Policy Iteration algorithm to compute an optimal policy for large Markov decision processes in the discounted reward criteria and under infinite horizon. The idea of this algorithm is based on the topology of the problem; moreover, an Open Multi-Processing (Open-MP) programming model is applied to attain efficient parallel performance in solving the Modified algorithm.
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