从强化学习中提取知识

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) Pub Date : 1999-07-10 DOI:10.1109/IJCNN.1999.833476

R. Sun

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引用次数: 8

摘要

本文主要研究强化学习器的知识提取问题。它解决了两种知识提取的方法:在神经强化学习器前提取明确的符号规则;以及从这些学习者中提取完整的计划。这种知识提取的优点包括:改进了学习(特别是使用规则提取方法);提高了学习结果的可用性。

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Knowledge extraction from reinforcement learning

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.

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IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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