English(EN) Finite-Time Convergence of Distributionally Robust Q-Learning with Linear Function Approximation

新的DRRL算法通过线性逼近实现有限时间收敛

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了一种新的分布鲁棒强化学习（DRRL）算法，即使在使用线性函数逼近的情况下也能提供有限时间收敛保证。该算法解决了现有DRRL方法的局限性，这些方法通常需要表格设置或特定的结构假设。新方法结合了目标网络和对偶函数逼近方案，利用矩跟踪批评者和后缀平均来实现收敛到最优鲁棒Q函数。 AI

影响为鲁棒强化学习提供了理论保证，有可能提高智能体在不确定环境中的性能。

排序理由该集群包含一篇详细介绍新算法及其理论收敛保证的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Saptarshi Mandal, Yashaswini Murthy, R. Srikant · 2026-06-16 04:00

具有线性函数逼近的分布鲁棒Q学习的有限时间收敛性

arXiv:2510.01721v3 Announce Type: replace Abstract: Distributionally robust reinforcement learning (DRRL) seeks policies that perform well when the deployment transition model differs from the nominal model generating the data. Most finite-sample guarantees for DRRL are tabular, …