Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars

This paper focuses on the generalization of exemplars (i.e., good rules) in the reinforcement learning framework and proposes Exemplar Generalization in Reinforcement Learning (EGRL) that extracts usual exemplars from a lot of exemplars provided as a prior knowledge and generalizes them by deleting...

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Bibliographic Details
Published in:Journal of advanced computational intelligence and intelligent informatics 2009-11, Vol.13 (6), p.683-690
Main Authors: Matsushima, Hiroyasu, Hattori, Kiyohiko, Takadama, Keiki
Format: Article
Language:English
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Summary:This paper focuses on the generalization of exemplars (i.e., good rules) in the reinforcement learning framework and proposes Exemplar Generalization in Reinforcement Learning (EGRL) that extracts usual exemplars from a lot of exemplars provided as a prior knowledge and generalizes them by deleting unnecessary exemplars (some exemplars overlap) as much as possible. Through intensive simulation of a simple cargo layout problem to validate EGRL effectiveness, the following implications have been revealed: (1) EGRL derives good performance with fewer exemplars than using the efficient numbers of exemplars and randomly selected exemplars and (2) integration of covering, deletion, and subsumption mechanisms in EGRL is critical for improving EGRL performance and generalization.
ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2009.p0683