English(EN) When Greedy Sampling Explores: KL-Regularized Contextual Bandits without Eluder-Dimension Dependence

新的老虎机算法实现了对数遗憾，且无逃逸维度依赖

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种新的KL正则化上下文老虎机方法，专注于同时具有奖励和偏好反馈的场景。他们的工作表明，一种简单的贪婪采样方法可以在不依赖逃逸维度的前提下实现对数遗憾。该方法直接从估计奖励导出的Gibbs策略中采样，分析揭示了贪婪采样与置信上界风格探索之间的权衡，具体取决于KL正则化的强度。 AI

影响这项研究可能带来更高效的强化学习算法在各种应用中。

排序理由该条目是一篇学术论文，详细介绍了一种新的上下文老虎机算法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Zichen Wang, Haoyang Hong, Huazheng Wang · 2026-09-15 04:00

当贪婪采样探索时：KL正则化上下文老虎机不依赖于Eluder维度

arXiv:2609.13564v1 Announce Type: new Abstract: We study KL-regularized contextual bandits under both reward and preference feedback. We show that greedy sampling can achieve logarithmic regret without explicit dependence on the eluder dimension. For reward feedback, we establish…