Parallelizing parallel rollout algorithm for solving Markov decision processes

Seon Wook Kim, Hyeong Soo Chang

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Parallel rollout is a formal method of combining multiple heuristic policies available to a sequential decision maker in the framework of Markov Decision Processes (MDPs). The method improves the performances of all of the heuristic policies adapting to the different stochastic system trajectories. From an inherent multi-level parallelism in the method, in this paper we propose a parallelized version of parallel rollout algorithm, and evaluate its performance on a multi-class task scheduling problem by using OpenMP and MPI programming model. We analyze and compare the performance in two versions of parallelized codes, e.g., OpenMP and MPI on several execution environment. We show that the performance using OpenMP API is higher than MPI due to lower overhead in data synchronization across processors.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMichael J. Voss
PublisherSpringer Verlag
Pages122-136
Number of pages15
ISBN (Print)9783540404354
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2716
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, S. W., & Chang, H. S. (2003). Parallelizing parallel rollout algorithm for solving Markov decision processes. In M. J. Voss (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 122-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2716). Springer Verlag. https://doi.org/10.1007/3-540-45009-2_10