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  TD(λ) converges with probability 1

Dayan, P., & Sejnowski, T. (1994). TD(λ) converges with probability 1. Machine Learning, 14(3), 295-301. doi:10.1007/BF00993978.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-D6E2-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-D6E3-C
Genre: Journal Article

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Dayan, P1, Author              
Sejnowski, TJ, Author
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 Abstract: The methods of temporal differences (Samuel, 1959; Sutton, 1984, 1988) allow an agent to learn accurate predictions of stationary stochastic future outcomes. The learning is effectively stochastic approximation based on samples extracted from the process generating the agent's future. Sutton (1988) proved that for a special case of temporal differences, the expected values of the predictions converge to their correct values, as large samples are taken, and Dayan (1992) extended his proof to the general case. This article proves the stronger result that the predictions of a slightly modified form of temporal difference learning converge with probability one, and shows how to quantify the rate of convergence.

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 Dates: 1994-03
 Publication Status: Published in print
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 Identifiers: DOI: 10.1007/BF00993978
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Title: Machine Learning
Source Genre: Journal
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Publ. Info: Dordrecht : Springer
Pages: - Volume / Issue: 14 (3) Sequence Number: - Start / End Page: 295 - 301 Identifier: ISSN: 0885-6125
CoNE: https://pure.mpg.de/cone/journals/resource/08856125