Optimal Bandit

Optimal bandit algorithms aim to maximize cumulative rewards in sequential decision-making problems where the outcome of each action is uncertain. Current research focuses on developing algorithms with improved regret bounds under various conditions, including volatile environments, improving bandits (where rewards increase with exploration), and extreme bandits (focused on maximizing the largest reward). These advancements leverage techniques like multiplicative updates, subsampling, and self-concordant barrier functions to achieve optimal or near-optimal performance, impacting fields like online advertising, clinical trials, and resource allocation. The development of data-dependent regret guarantees and algorithms applicable to diverse comparator classes broadens the applicability of these methods.