Nonstationary Stochastic Bandits: UCB Policies and Minimax Regret

We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distributions of rewards associated with arms are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in th...

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發表在:	IEEE Open Journal of Control Systems
Main Authors:	Lai Wei, Vaibhav Srivastava
格式:	Article
語言:	英语
出版:	IEEE 2024-01-01
主題:	Heavy-tailed distributions minimax regret nonstationary multiarmed bandit upper-confidence bound variation budget
在線閱讀:	https://ieeexplore.ieee.org/document/10460198/