English(EN) Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling

新算法Exp-KL-MS解决了多臂老虎机挑战

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

研究人员开发了一种名为指数库尔巴克-莱布勒美拉德采样（Exp-KL-MS）的新算法来解决多臂老虎机问题。该算法旨在同时满足多个性能标准，包括渐近最优性、具有 $\sqrt{\ln (K)}$ 因子的极小极大最优性、Sub-UCB以及方差自适应遗憾界限。Exp-KL-MS算法利用最小经验散度框架，其中探索概率由逆温度函数和臂的经验均值之间的库尔巴克-莱布勒散度决定。 AI

影响为老虎机问题中的优化决策引入了一种新颖的算法，有可能提高强化学习场景的效率。

排序理由该集群包含一篇详细介绍机器学习问题新算法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Hao Qin, Kwang-Sung Jun, Chicheng Zhang · 2026-11-05 05:00

使用指数库尔巴克-莱布勒·梅拉德采样实现多臂老虎机的适应性和最优性

arXiv:2502.14379v3 Announce Type: replace Abstract: We study the problem of $K$-armed bandits with reward distributions belonging to a one-parameter exponential distribution family. In the literature, several criteria have been proposed to evaluate the performance of such algorit…

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使用指数库尔巴克-莱布勒·梅拉德采样实现多臂老虎机的适应性和最优性

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