Tight Regret

Tight regret in online learning focuses on designing algorithms that minimize the difference between an agent's cumulative loss and that of the optimal strategy, aiming for regret bounds that are as small as possible, ideally constant or logarithmic in the time horizon. Current research emphasizes achieving tight regret bounds in various settings, including contextual bandits with post-serving information, reinforcement learning with lookahead, and batched bandits with partial feedback, often employing techniques like reward imputation, sketching, and robust versions of established lemmas. These advancements improve the efficiency and robustness of online learning algorithms, impacting applications such as recommendation systems, online advertising, and resource allocation where efficient decision-making under uncertainty is crucial.