Intrinsically motivated learning of hierarchical collections of skills

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Intrinsically motivated learning of hierarchical collections of skills (2004)

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by Andrew G. Barto

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173 - 16 self

BibTeX

@INPROCEEDINGS{Barto04intrinsicallymotivated,
    author = {Andrew G. Barto},
    title = {Intrinsically motivated learning of hierarchical collections of skills},
    booktitle = {},
    year = {2004},
    pages = {112--119}
}

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Abstract

Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies. 1

Keyphrases

hierarchical collection practical problem present initial result new concept skill hierarchy computational study competent autonomous entity algorithmic advance artificial agent reusable skill wide range motivated behavior computational reinforcement learning competent autonomy

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