AINeutralarXiv – CS AI · Mar 34/104
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Near-Optimal Regret for KL-Regularized Multi-Armed Bandits
Researchers developed a new analysis of KL-regularized multi-armed bandits (MABs) using KL-UCB algorithm, achieving near-optimal regret bounds. The study provides the first high-probability regret bound with linear dependence on the number of arms and establishes matching lower bounds, offering comprehensive understanding across all regularization regimes.
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