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Near-optimal Per-Action Regret Bounds for Sleeping Bandits
March 5, 2024, 2:42 p.m. | Quan Nguyen, Nishant A. Mehta
cs.LG updates on arXiv.org arxiv.org
Abstract: We derive near-optimal per-action regret bounds for sleeping bandits, in which both the sets of available arms and their losses in every round are chosen by an adversary. In a setting with $K$ total arms and at most $A$ available arms in each round over $T$ rounds, the best known upper bound is $O(K\sqrt{TA\ln{K}})$, obtained indirectly via minimizing internal sleeping regrets. Compared to the minimax $\Omega(\sqrt{TA})$ lower bound, this upper bound contains an extra multiplicative …
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