EL-F Algorithm

A multi-armed bandit algorithm based on empirical likelihood extended with a g-Forcing strategy. It requires no prior knowledge of the reward scale or tail parameters, and provides three layers of theoretical guarantees: asymptotic optimality, a finite-time expected regret upper bound, and tail risk characterization.