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Abstract:
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.