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Computer Science > Machine Learning

arXiv:2109.04033 (cs)
[Submitted on 9 Sep 2021 (v1), last revised 22 Jan 2024 (this version, v4)]

Title:New Versions of Gradient Temporal Difference Learning

Authors:Donghwan Lee, Han-Dong Lim, Jihoon Park, Okyong Choi
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Abstract:Sutton, Szepesvári and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training. The goal of this paper is (a) to propose some variants of GTDs with extensive comparative analysis and (b) to establish new theoretical analysis frameworks for the GTDs. These variants are based on convex-concave saddle-point interpretations of GTDs, which effectively unify all the GTDs into a single framework, and provide simple stability analysis based on recent results on primal-dual gradient dynamics. Finally, numerical comparative analysis is given to evaluate these approaches.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.04033 [cs.LG]
  (or arXiv:2109.04033v4 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.04033

Submission history

From: Donghwan Lee [view email]
[v1] Thu, 9 Sep 2021 04:48:54 UTC (75 KB)
[v2] Thu, 16 Jun 2022 11:47:55 UTC (75 KB)
[v3] Fri, 17 Jun 2022 02:44:12 UTC (75 KB)
[v4] Mon, 22 Jan 2024 13:53:09 UTC (76 KB)
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