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Permanent link (DOI): https://doi.org/10.7939/R3PR7MW1W

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The Theoretical Foundation for Incremental Least-Squares Temporal Difference Learning Open Access

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Author or creator
Zinkevich, Martin
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Subject/Keyword
Incremental Least-Squares Temporal Difference Learning
Type of item
Report
Language
English
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Time
Description
Technical report TR06-25. In this paper we present a mathematical foundation for Incremental Least-Squares Temporal Difference Learning (iLSTD) for policy evaluation in reinforcement learning with linear function approximation. iLSTD is an incremental method for achieving results similar to LSTD, the data-efficient, least-squares version of temporal difference learning, without incurring the full cost of the LSTD computation. Here, we give a technical foundation for the asymptotic properties of iLSTD.
Date created
2006
DOI
doi:10.7939/R3PR7MW1W
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Creative Commons Attribution 3.0 Unported
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