Correlated Multi-Armed Bandits with A Latent Random Source

Multi-armed bandit models are widely studied sequential decision-making problems that exemplify the exploration-exploitation trade-off. We study a novel correlated multi-armed bandit model where the rewards obtained from the arms are functions of a common latent random variable. We propose and analyze the performance of the C-UCB algorithm that leverages the correlations between arms to reduce the cumulative regret (i.e., to increase the total reward obtained after T rounds). Unlike the standard UCB algorithm that pulls all sub-optimal arms O(log T) times, the C-UCB algorithm takes only O(1) times to identify that some arms, which we refer to as non-competitive arms, are optimal. Thus, we effectively reduce a K-armed bandit problem to a C + 1−armed bandit problem with C < K denoting the number of competitive, where C can be computed from the reward functions. A key consequence is that when C = 0, our algorithm achieves a constant (i.e., O(1)) regret instead of the standard O(log T) scaling with the number of rounds T . Establishing lower bounds for the regret, we show that the C-UCB algorithm is order-wise optimal and demonstrate its superiority against other algorithms via numerical simulations.