  We study distributed cooperative decision-making under the explore-exploit
tradeoff in the multiarmed bandit (MAB) problem. We extend the state-of-the-art
frequentist and Bayesian algorithms for single-agent MAB problems to
cooperative distributed algorithms for multi-agent MAB problems in which agents
communicate according to a fixed network graph. We rely on a running consensus
algorithm for each agent's estimation of mean rewards from its own rewards and
the estimated rewards of its neighbors. We prove the performance of these
algorithms and show that they asymptotically recover the performance of a
centralized agent. Further, we rigorously characterize the influence of the
communication graph structure on the decision-making performance of the group.
