Knowledge extraction from reinforcement learning

Abstract

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