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In this paper, we generalize the concept of heavy-tailed multi-armed bandits to adversarial environments, and develop robust best-of-both-worlds algorithms for heavy-tailed multi-armed bandits (MAB), where losses have α-th (1<α≤ 2) moments bounded by σ^α, while the variances may not exist. Specifically, we design an algorithm , when the heavy-tail parameters α and σ are known to the agent, simultaneously achieves the optimal regret for both stochastic and adversarial environments, withoutarXiv:2201.11921v2 fatcat:24fbsknv6reuriueuep4cexzfi