  Multiplayer bandits have recently been extensively studied because of their
application to cognitive radio networks.
  While the literature mostly considers synchronous players, radio networks
(e.g. for IoT) tend to have asynchronous devices. This motivates the harder,
asynchronous multiplayer bandits problem, which was first tackled with an
explore-then-commit (ETC) algorithm (see Dakdouk, 2022), with a regret
upper-bound in $\mathcal{O}(T^{\frac{2}{3}})$. Before even considering
decentralization, understanding the centralized case was still a challenge as
it was unknown whether getting a regret smaller than $\Omega(T^{\frac{2}{3}})$
was possible.
  We answer positively this question, as a natural extension of UCB exhibits a
$\mathcal{O}(\sqrt{T\log(T)})$ minimax regret.
  More importantly, we introduce Cautious Greedy, a centralized algorithm that
yields constant instance-dependent regret if the optimal policy assigns at
least one player on each arm (a situation that is proved to occur when arm
means are close enough). Otherwise, its regret increases as the sum of
$\log(T)$ over some sub-optimality gaps. We provide lower bounds showing that
Cautious Greedy is optimal in the data-dependent terms.
  Therefore, we set up a strong baseline for asynchronous multiplayer bandits
and suggest that learning the optimal policy in this problem might be easier
than thought, at least with centralization.
