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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.

Exemplar Generalization in Reinforcement Learning: Improving Performance with Fewer Exemplars