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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
Year of Publication: 2016
Sanaa Chafik, Cherki Daoui

Sanaa Chafik and Cherki Daoui. Article: A Modified Policy Iteration Algorithm for Discounted Reward Markov Decision Processes. International Journal of Computer Applications 133(10):28-33, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Sanaa Chafik and Cherki Daoui},
	title = {Article: 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},
	month = {January},
	note = {Published by 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.


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Markov Decision Processe; Discounted reward criterion; Policy Iteration algorithm; Open Multi-Processing; shared memory; Parallelizing.