Knowledge extraction from reinforcement learning

Authors:
Ron Sun
Affiliations:
Univ. of Missouri-Columbia, Columbia, MI
Venue:
New learning paradigms in soft computing
Year:
2002

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Abstract

This chapter is concerned with knowledge extraction from reinforcement learners. It addresses two approaches towards knowledge extraction: the extraction of explicit, symbolic rules from neural reinforcement learners, and the extraction of complete plans from such learners. The advantages of such knowledge extraction include (1) the improvement of learning (especially with the rule extraction approach), and (2) the improvement of the usability of results of learning.