Improved Regret Bounds for Bandits with Expert Advice

Abstract

In this research note, we revisit the bandits with expert advice problem. Under a restricted feedback model, we prove a lower bound of order [KT ln(N/K)]^1/2 for the worst-case regret, where K is the number of actions, N > K the number of experts, and T the time horizon. This matches a previously known upper bound of the same order and improves upon the best available lower bound of [KT ln(N)/ln(K)]^1/2.  For the standard feedback model, we prove a new instance-based upper bound that depends on the agreement between the experts and provides a logarithmic improvement compared to prior results.