English(EN) Sample complexity of variance-reduced policy gradient: weaker assumptions and lower bounds

防御性策略梯度算法提高了强化学习的样本复杂度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

一种名为防御性策略梯度（DPG）的新算法已被开发出来，它改进了现有的强化学习方差缩减策略梯度方法。与先前需要对方差做出不切实际假设的方法不同，DPG 在没有这些限制的情况下实现了 O(ε−3) 的改进样本复杂度。该研究还为策略优化建立了理论下界，表明 DPG 的更快收敛速度是最优的。 AI

影响引入了一种更具样本效率的强化学习算法，有可能在复杂环境中加速训练时间和提高模型性能。

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arXiv cs.LG TIER_1 English(EN) · Gabor Paczolay, Matteo Papini, Alberto Maria Metelli, Istvan Harmati, Marcello Restelli · 2026-10-05 04:00

方差缩减策略梯度方法的样本复杂度：更弱的假设和更低的界限

arXiv:2610.03165v1 Announce Type: new Abstract: Several variance-reduced versions of REINFORCE based on importance sampling achieve an improved $O(\epsilon^{-3})$ sample complexity to find an $\epsilon$-stationary point, under an unrealistic assumption on the variance of the impo…