The Theoretical Foundation for Incremental Least-Squares Temporal Difference Learning
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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. | TRID-ID TR06-25
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http://purl.org/coar/resource_type/c_93fc
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en