Mixed Acceleration Techniques for Solving Quickly Stochastic Shortest-Path Markov Decision Processes

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1. Verfasser: M. de G. García-Hernández
Format: Artículo científico
Sprache:en
Veröffentlicht: Universidad Nacional Autónoma de México 2011
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author M. de G. García-Hernández
author_facet M. de G. García-Hernández
contents Mixed Acceleration Techniques for Solving Quickly Stochastic Shortest-Path Markov Decision Processes M. de G. García-Hernández J. Ruiz-Pinales E. Onaindía S. Ledesma-Orozco J. G. Aviña-Cervantes E. Alvarado-Méndez A. Reyes-Ballesteros Ingeniería prioritization acceleration techniques Markov decision processes In this paper we propose the combination of accelerated variants of value iteration mixed with improved prioritized sweeping for the fast solution of stochastic shortest-path Markov decision processes. Value iteration is a classical algorithm for solving Markov decision processes, but this algorithm and its variants are quite slow for solving considerably large problems. In order to improve the solution time, acceleration techniques such as asynchronous updates, prioritization and prioritized sweeping have been explored in this paper. A topological reordering algorithm was also compared with static reordering. Experimental results obtained on finite state and action-space stochastic shortest-path problems show that our approach achieves a considerable reduction in the solution time with respect to the tested variants of value iteration. For instance, the experiments showed in one test a reduction of 5.7 times with respect to value iteration with asynchronous updates. 2011 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47419293002 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.9
format Artículo científico
id redalyc_47419293002
institution Redalyc
language en
publishDate 2011
publisher Universidad Nacional Autónoma de México
spellingShingle Mixed Acceleration Techniques for Solving Quickly Stochastic Shortest-Path Markov Decision Processes
M. de G. García-Hernández
Ingeniería
prioritization
acceleration techniques
Markov decision processes
Mixed Acceleration Techniques for Solving Quickly Stochastic Shortest-Path Markov Decision Processes M. de G. García-Hernández J. Ruiz-Pinales E. Onaindía S. Ledesma-Orozco J. G. Aviña-Cervantes E. Alvarado-Méndez A. Reyes-Ballesteros Ingeniería prioritization acceleration techniques Markov decision processes In this paper we propose the combination of accelerated variants of value iteration mixed with improved prioritized sweeping for the fast solution of stochastic shortest-path Markov decision processes. Value iteration is a classical algorithm for solving Markov decision processes, but this algorithm and its variants are quite slow for solving considerably large problems. In order to improve the solution time, acceleration techniques such as asynchronous updates, prioritization and prioritized sweeping have been explored in this paper. A topological reordering algorithm was also compared with static reordering. Experimental results obtained on finite state and action-space stochastic shortest-path problems show that our approach achieves a considerable reduction in the solution time with respect to the tested variants of value iteration. For instance, the experiments showed in one test a reduction of 5.7 times with respect to value iteration with asynchronous updates. 2011 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47419293002 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.2 Vol.9
title Mixed Acceleration Techniques for Solving Quickly Stochastic Shortest-Path Markov Decision Processes
topic Ingeniería
prioritization
acceleration techniques
Markov decision processes
url https://www.redalyc.org/articulo.oa?id=47419293002