International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 133 - Issue 10 |
Published: January 2016 |
Authors: Sanaa Chafik, Cherki Daoui |
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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
@article{ 10.5120/ijca2016908033, author = { Sanaa Chafik,Cherki Daoui }, title = { A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes }, journal = { International Journal of Computer Applications }, year = { 2016 }, volume = { 133 }, number = { 10 }, pages = { 28-33 }, doi = { 10.5120/ijca2016908033 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2016 %A Sanaa Chafik %A Cherki Daoui %T A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes%T %J International Journal of Computer Applications %V 133 %N 10 %P 28-33 %R 10.5120/ijca2016908033 %I Foundation of Computer Science (FCS), NY, USA
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.