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 asynchrono...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | inglés |
| OAI Identifier: | oai:ojs2.localhost:article/493 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/493 |
| Access Level: | acceso abierto |
| Palabra clave: | Markov decision processes association rules acceleration procedures Procesos de decisión de Markov reglas de asociación procesos de aceleración |
| Sumario: | 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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