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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Multistrategy Learning for Robot Behaviours
verfasst von : Claude Sammut, Tak Fai Yik
Erschienen in: Advances in Machine Learning I
Verlag: Springer Berlin Heidelberg
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Pure reinforcement learning does not scale well to domains with many degrees of freedom and particularly to continuous domains. In this paper, we introduce a hybrid method in which a symbolic planner constructs an approximate solution to a control problem. Subsequently, a numerical optimisation algorithm is used to refine the qualitative plan into an operational policy. The method is demonstrated on the problem of learning a stable walking gait for a bipedal robot. We use this approach to illustrate the benefits of a multistrategy approach to robot learning.