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Reports (Research Report) Year : 2013

Off-policy Learning with Eligibility Traces: A Survey

Matthieu Geist
Bruno Scherrer

Abstract

In the framework of Markov Decision Processes, off-policy learning, that is the problem of learning a linear approximation of the value function of some fixed policy from one trajectory possibly generated by some other policy. We briefly review on-policy learning algorithms of the literature (gradient-based and least-squares-based), adopting a unified algorithmic view. Then, we highlight a systematic approach for adapting them to off-policy learning with eligibility traces. This leads to some known algorithms - off-policy LSTD(λ), LSPE(λ), TD(λ), TDC/GQ(λ) - and suggests new extensions - off-policy FPKF(λ), BRM(λ), gBRM(λ), GTD2(λ). We describe a comprehensive algorithmic derivation of all algorithms in a recursive and memory-efficent form, discuss their known convergence properties and illustrate their relative empirical behavior on Garnet problems. Our experiments suggest that the most standard algorithms on and off-policy LSTD(λ)/LSPE(λ) - and TD(λ) if the feature space dimension is too large for a least-squares approach - perform the best.
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Dates and versions

hal-00644516 , version 1 (24-11-2011)
hal-00644516 , version 2 (12-04-2013)

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Matthieu Geist, Bruno Scherrer. Off-policy Learning with Eligibility Traces: A Survey. [Research Report] 2013, pp.43. ⟨hal-00644516v2⟩
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