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Efficient skill learning using abstraction selection

Publication ,  Journal Article
Konidaris, G; Barto, A
Published in: IJCAI International Joint Conference on Artificial Intelligence
January 1, 2009

We present an algorithm for selecting an appropriate abstraction when learning a new skill. We show empirically that it can consistently select an appropriate abstraction using very little sample data, and that it significantly improves skill learning performance in a reasonably large real-valued reinforcement learning domain.

Duke Scholars

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

Publication Date

January 1, 2009

Start / End Page

1107 / 1112
 

Citation

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Konidaris, G., & Barto, A. (2009). Efficient skill learning using abstraction selection. IJCAI International Joint Conference on Artificial Intelligence, 1107–1112.
Konidaris, G., and A. Barto. “Efficient skill learning using abstraction selection.” IJCAI International Joint Conference on Artificial Intelligence, January 1, 2009, 1107–12.
Konidaris G, Barto A. Efficient skill learning using abstraction selection. IJCAI International Joint Conference on Artificial Intelligence. 2009 Jan 1;1107–12.
Konidaris, G., and A. Barto. “Efficient skill learning using abstraction selection.” IJCAI International Joint Conference on Artificial Intelligence, Jan. 2009, pp. 1107–12.
Konidaris G, Barto A. Efficient skill learning using abstraction selection. IJCAI International Joint Conference on Artificial Intelligence. 2009 Jan 1;1107–1112.

Published In

IJCAI International Joint Conference on Artificial Intelligence

ISSN

1045-0823

Publication Date

January 1, 2009

Start / End Page

1107 / 1112