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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Making a Reinforcement Learning Agent Believe
verfasst von : Klaus Häming, Gabriele Peters
Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2012
Verlag: Springer Berlin Heidelberg
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We recently explored the benefits of a reinforcement learning agent which is supplemented by a symbolic learning level. This second level is represented in the symbolic form of Spohn’s ranking functions. Given this context, we discuss in this paper the creation of symbolic rules from a
Q
-function. We explore several alternatives and show that the rule generation greatly influences the performance of the agent. We provide empirical evidence about which approach to favor. Additionally, the rules created by the considered application are shown to be plausible and understandable.