  We study the problem of stochastic bandits with adversarial corruptions in
the cooperative multi-agent setting, where $V$ agents interact with a common
$K$-armed bandit problem, and each pair of agents can communicate with each
other to expedite the learning process. In the problem, the rewards are
independently sampled from distributions across all agents and rounds, but they
may be corrupted by an adversary. Our goal is to minimize both the overall
regret and communication cost across all agents. We first show that an additive
term of corruption is unavoidable for any algorithm in this problem. Then, we
propose a new algorithm that is agnostic to the level of corruption. Our
algorithm not only achieves near-optimal regret in the stochastic setting, but
also obtains a regret with an additive term of corruption in the corrupted
setting, while maintaining efficient communication. The algorithm is also
applicable for the single-agent corruption problem, and achieves a high
probability regret that removes the multiplicative dependence of $K$ on
corruption level. Our result of the single-agent case resolves an open question
from Gupta et al. [2019].
