Peter Stone's Selected Publications

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Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping

Yaxin Liu and Peter Stone. Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 415–20, July 2006.
AAAI 2006

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Abstract

Transfer learning concerns applying knowledge learned in one task (the source) to improve learning another related task (the target). In this paper, we use structure mapping, a psychological and computational theory about analogy making, to find mappings between the source and target tasks and thus construct the transfer functional automatically. Our structure mapping algorithm is a specialized and optimized version of the structure mapping engine and uses heuristic search to find the best maximal mapping. The algorithm takes as input the source and target task specifications represented as qualitative dynamic Bayes networks, which do not need probability information. We apply this method to the Keepaway task from RoboCup simulated soccer and compare the result from automated transfer to that from handcoded transfer.

BibTeX Entry

@InProceedings{AAAI06-yaxin,
	author="Yaxin Liu and Peter Stone",
	title="Value-Function-Based Transfer for Reinforcement Learning Using Structure Mapping",
        booktitle="Proceedings of the Twenty-First National Conference on Artificial Intelligence",
        month="July",year="2006",
	pages="415--20",
	abstract={
                  Transfer learning concerns applying knowledge
                  learned in one task (the source) to improve learning
                  another related task (the target). In this paper, we
                  use structure mapping, a psychological and
                  computational theory about analogy making, to find
                  mappings between the source and target tasks and
                  thus construct the transfer functional
                  automatically.  Our structure mapping algorithm is a
                  specialized and optimized version of the structure
                  mapping engine and uses heuristic search to find the
                  best maximal mapping.  The algorithm takes as input
                  the source and target task specifications
                  represented as qualitative dynamic Bayes networks,
                  which do not need probability information.  We apply
                  this method to the Keepaway task from RoboCup
                  simulated soccer and compare the result from
                  automated transfer to that from handcoded transfer.
	},
        wwwnote={<a href="http://www.aaai.org/Conferences/AAAI/aaai06.php">AAAI 2006</a>},
}

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