English(EN) Fast Rates for Offline Q-Learning with KL Regularization

新的Q学习算法通过KL正则化实现快速收敛率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

研究人员开发了用于有限时间范围马尔可夫决策过程（MDP）的离线Q学习的新算法，并结合了 Kullback-Leibler (KL) 正则化。所提出的 KL-regularized Q-Learning (KLQ) 算法在没有显式悲观主义的情况下实现了 \(\widetilde{O}(1/\epsilon)\) 的样本复杂度，尽管对正则化参数和时间范围有指数依赖性。随后的 KL-regularized Pessimistic Q-Learning (KLPQ) 算法保持了相同的收敛率，同时消除了这种依赖性，从而提高了整体样本复杂度。这些发现代表了表格型有限时间范围 KL 正则化 MDP 中离线Q学习的首次快速收敛率保证。 AI

影响为离线强化学习建立了新的理论基础，有可能提高现实世界顺序决策任务的效率。

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arXiv cs.AI TIER_1 English(EN) · Yize Hao, Guangxin Jiang, Shuang Qiu · 2026-10-15 04:00

KL正则化下离线Q学习的快速收敛

arXiv:2610.18839v1 Announce Type: cross Abstract: This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies have shown that KL regularization can yield fast rates in offline decision-making pr…

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KL正则化下离线Q学习的快速收敛

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