讲座题目：Empirical Gittins Index Strategies with ε-Explorations for Multi-armed Bandit Problems

主 讲 人：华东师范大学吴贤毅教授

讲座时间：2023年5月18日（周四）13：40-14：40

讲座地点：6号学院楼402会议室

主办单位：伟德BETVlCTOR1946源于英国  浙江省2011“数据科学与大数据分析协同创新中心”

摘 要：

The machine learning/statistics literature has so far considered largely multi-armed bandit (MAB) problems in which the rewards from every arm are assumed independent and identically distributed. For more general MAB models in which every arm evolves according to a rewarded Markov process, it is well known the optimal policy is to pull an arm with the highest Gittins index. When the underlying distributions are unknown, an empirical Gittins index rule withε-exploration (abbreviated as empiricalε-Gittinx index rule) is proposed to solve such MAB problems. This procedure is constructed by combining the idea ofε-exploration (for exploration) and empirical Gittins indices (for exploitation) computed by applying the Largest-Remaining-Index algorithm to the estimated underlying distribution. The convergence of empirical Gittins indices to the true Gittins indices and expected discounted total rewards of the empiricalε-Gittinx index rule to those of the oracle Gittins index rule is provided. A numerical simulation study is demonstrated to show the behavior of the proposed policies, and its performance over theε-mean reward is discussed.

主讲人简介：

吴贤毅，博士、教授，华东师范大学经济与管理学部统计学院教授，研究及教学内容涉及统计学、机器学习/人工智能、非寿险精算学、随机调度等领域，在国际主流学术杂志发表过学术论文70余篇，在国内外出版社出版过专著两部，教材一部。

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