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The Theoretical Foundation for Incremental Least-Squares Temporal Difference Learning

  • Author(s) / Creator(s)
  • 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

  • Date created
    2006
  • Subjects / Keywords
  • Type of Item
    Report
  • DOI
    https://doi.org/10.7939/R3PR7MW1W
  • License
    Attribution 3.0 International