English(EN) Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

新研究为离线上下文老虎机提供了更快的收敛速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

一篇新发表在arXiv上的研究论文介绍了一种简化的前向KL正则化离线上下文老虎机分析方法。该研究首次提供了样本复杂度为O(epsilon^{-1})的上限界，在单策略集中性条件下，适用于表格和一般函数逼近设置。这项工作解决了现有分析中前向KL目标函数收敛速度为O(epsilon^{-2})的不足，并包含了速率最优的下限界以证实新上限界的紧密性。 AI

影响这项研究可能为数据有限场景下的决策制定带来更高效的算法，从而可能改进个性化推荐或资源分配等领域的应用。

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在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Quanquan Gu · 2026-11-06 05:00

基于前向KL正则化和单策略集中性假设的离线上下文老虎机快速收敛

arXiv:2605.09214v2 Announce Type: replace-cross Abstract: \emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have demonstrated $\epsilon^{-1}$-type fast rates for decisi…

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基于前向KL正则化和单策略集中性假设的离线上下文老虎机快速收敛

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