English(EN) On Convergence of Gradient Temporal-Difference Learning with Regularized Corrections and Eligibility Traces

新的TD学习算法收敛性已确立

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员确立了一种改进的梯度时序差分（TD）学习算法，称为约束TDRC($\lambda$)，在非i.i.d.离策略数据下的收敛性。这项工作还引入了TDRC($\lambda$)-Huber，这是一个旨在减轻对大Bellman误差敏感性的扩展，同时保持TDRC($\lambda$)的固定点。收敛性证明利用了随机逼近理论中的基于ODE的弱收敛方法，提供了可计算的充分条件，并识别了一个共享的基于正则化的收敛机制。 AI

影响为先进的强化学习算法建立了理论收敛保证，有可能提高其在复杂环境中的稳定性和性能。

排序理由详细介绍机器学习算法理论进展的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jingyi Jin, Martha White, Hao Wang · 2026-11-05 05:00

关于带正则化修正和资格迹的梯度时序差分学习收敛性

arXiv:2611.05869v1 Announce Type: new Abstract: Temporal-difference (TD) learning underlies many prediction and control algorithms, but can diverge when combined with off-policy learning, bootstrapping, and function approximation. TD with Regularized Corrections (TDRC) is theoret…

本文如何被排名

报道来源 [1]

关于带正则化修正和资格迹的梯度时序差分学习收敛性

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