Upper Confidence Bound

The Upper Confidence Bound (UCB) algorithm is a widely used approach in reinforcement learning and related fields, aiming to balance exploration and exploitation in sequential decision-making problems. Current research focuses on extending UCB to more complex scenarios, including multi-armed bandits with combinatorial actions, multivariant rewards, and non-stationary environments, often incorporating Bayesian methods or modifications like power mean estimators for improved accuracy and efficiency. These advancements are driving progress in diverse applications such as recommender systems, reinforcement learning from human feedback, and multi-agent systems, where efficient and robust decision-making under uncertainty is crucial. Furthermore, research explores integrating UCB with privacy-preserving techniques and addressing challenges like cold-start problems.