UCB Algorithm

The Upper Confidence Bound (UCB) algorithm is a widely used method for solving multi-armed bandit problems, aiming to maximize cumulative rewards by balancing exploration and exploitation of different options. Current research focuses on extending UCB to more complex scenarios, including contextual bandits, combinatorial bandits, and federated learning settings, often incorporating Gaussian processes or other advanced model architectures to improve efficiency and handle diverse data structures. These advancements have significant implications for various fields, such as online advertising, recommendation systems, and resource allocation problems, by enabling more effective and efficient decision-making under uncertainty.