Bandit Model

Bandit models are a class of online learning algorithms aiming to maximize cumulative rewards by sequentially selecting actions (arms) from a set with unknown reward distributions. Current research emphasizes extensions to complex scenarios, including federated learning (distributing learning across multiple agents), domain adaptation (transferring knowledge between different data distributions), and incorporating contextual information and user states for more nuanced decision-making. These advancements are driving improvements in diverse applications such as personalized recommendations, online advertising, and reinforcement learning, particularly in high-dimensional settings where efficient exploration and exploitation strategies are crucial.