Optimal Arm

Optimal arm identification in multi-armed bandit problems focuses on efficiently finding the best-performing option from a set of alternatives, minimizing the number of trials needed. Current research emphasizes developing algorithms robust to various challenges, including fairness constraints, adversarial attacks, resource limitations, and non-stationary environments, often employing techniques like successive elimination, upper confidence bounds, and Follow-the-Regularized-Leader. These advancements are crucial for improving the efficiency and reliability of decision-making in diverse applications ranging from clinical trials and online advertising to resource allocation and federated learning. The field is also exploring collaborative settings and the impact of communication costs on optimal arm identification.