Best Arm Identification

Best arm identification (BAI) focuses on efficiently finding the arm (option) with the highest expected reward in a multi-armed bandit problem, aiming to minimize the number of trials needed to achieve a desired confidence level. Recent research emphasizes developing algorithms that are both theoretically optimal (matching lower bounds on sample complexity) and computationally efficient, often employing techniques like upper confidence bounds (UCB), Thompson sampling, and successive elimination, with variations tailored to Bayesian settings, fixed budgets, and resource constraints. BAI has significant implications for various fields, including clinical trials, A/B testing, and hyperparameter optimization, by enabling faster and more informed decision-making under uncertainty.