Bandit Algorithm

Bandit algorithms are a class of online learning algorithms designed to optimize sequential decision-making under uncertainty, aiming to maximize cumulative rewards by balancing exploration (trying different options) and exploitation (choosing the currently best option). Current research focuses on extending bandit algorithms to handle more complex scenarios, including partially observable contexts, non-stationary environments, heterogeneous agents, and incorporating offline data or user preferences, often employing Thompson sampling, UCB variants, and other novel algorithms tailored to specific problem structures (e.g., generalized linear models, tensor representations). These advancements have significant implications for various fields, such as personalized healthcare, recommendation systems, online advertising, and financial portfolio management, by enabling more efficient and effective decision-making in dynamic and uncertain environments.