A Tight Lower Bound for Non-stochastic Multi-armed Bandits with Expert Advice

We determine the minimax optimal expected regret in the classic non-stochastic multi-armed bandit with expert advice problem, by proving a lower bound that matches the upper bound of [Kale ’14]. The two bounds determine the minimax optimal expected regret to be $\Theta\left( \sqrt{T K \log \frac{N}{K} } \right)$, where $K$ is the number of arms, $N$ is the number of experts, and $T$ is the time horizon.