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  Improving Generalization for Temporal Difference Learning: The Successor Representation

Dayan, P. (1993). Improving Generalization for Temporal Difference Learning: The Successor Representation. Neural computation, 5(4), 613-624. doi:10.1162/neco.1993.5.4.613.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-D707-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-D708-3
Genre: Journal Article

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Dayan, P1, Author              
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1External Organizations, ou_persistent22              

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 Abstract: Estimation of returns over time, the focus of temporal difference (TD) algorithms, imposes particular constraints on good function approximators or representations. Appropriate generalization between states is determined by how similar their successors are, and representations should follow suit. This paper shows how TD machinery can be used to learn such representations, and illustrates, using a navigation task, the appropriately distributed nature of the result.

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 Dates: 1993-07
 Publication Status: Published in print
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 Identifiers: DOI: 10.1162/neco.1993.5.4.613
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 5 (4) Sequence Number: - Start / End Page: 613 - 624 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591