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0111 · Apr 202319922001200920172026
1 result for “KL-MS”
Kullback-Leibler Maillard Sampling for Multi-armed Bandits with Bounded Rewardscs.LG

KL-MS improves regret bounds for multi-armed bandits with bounded rewards.

problem Designing efficient exploration algorithms for multi-armed bandits with bounded rewards.
method Kullback-Leibler Maillard Sampling (KL-MS) for multi-armed bandits with bounded rewards.
result KL-MS achieves a worst-case regret bound of O(μ∗(1−μ∗)KTln⁡K+Kln⁡T)O(\sqrt{μ^*(1-μ^*) K T \ln K} + K \ln T).
multi-armed banditsbounded rewardsKL-MSregret boundBernoulli rewards
2023-04-28abs ↗pdf ↗