Learning How to Do Things with Imitation

Aris Alissandrakis, Chrystopher L. Nehaniv, and Kerstin Dautenhahn

In this paper we discuss how agents can learn to do things by imitating other agents. Especially we look at how the use of different metrics and sub-goal granularity can affect the imitation results. We use a computer model of a chess world as a test-bed to also illustrate issues that arise when there is dissimilar embodiment between the demonstrator and the imitator agents.


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