Machine Learning

, Volume 49, Issue 2–3, pp 233–246 | Cite as

Technical Update: Least-Squares Temporal Difference Learning

  • Justin A. Boyan


TD.λ/ is a popular family of algorithms for approximate policy evaluation in large MDPs. TD.λ/ works by incrementally updating the value function after each observed transition. It has two major drawbacks: it may make inefficient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and λ = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto (1996, Machine learning, 22:1–3, 33–57) eliminates all stepsize parameters and improves data efficiency.

This paper updates Bradtke and Barto's work in three significant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from λ = 0 to arbitrary values of λ; at the extreme of λ = 1, the resulting new algorithm is shown to be a practical, incremental formulation of supervised linear regression. Third, it presents a novel and intuitive interpretation of LSTD as a model-based reinforcement learning technique.

reinforcement learning temporal difference learning value function approximation linear least-squares methods 


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Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Justin A. Boyan
    • 1
  1. 1.ITA SoftwareCambridgeUSA

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