Acceleration of association-rule based markov decision processes
In this paper, we present a new approach for the estimation of Markov decision processes based on efficient association rule mining techniques such as Apriori. For the fastest solution of the resulting association-rule based Markov decision process, several accelerating procedures such as asynchronous updates and prioritization using a static ordering have been applied. A new criterion for state reordering in decreasing order of maximum reward is also compared with a modified topological reordering algorithm. Experimental results obtained on a finite state and action-space stochastic shortest path problem demonstrate the feasibility of the new approach.
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| Main Authors: | , , , , , |
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| Format: | Digital revista |
| Language: | English |
| Published: |
Universidad Nacional Autónoma de México, Instituto de Ciencias Aplicadas y Tecnología
2009
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| Online Access: | http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1665-64232009000300008 |
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