  We study a cooperative multi-agent multi-armed bandits with M agents and K
arms. The goal of the agents is to minimized the cumulative regret. We adapt a
traditional Thompson Sampling algoirthm under the distributed setting. However,
with agent's ability to communicate, we note that communication may further
reduce the upper bound of the regret for a distributed Thompson Sampling
approach. To further improve the performance of distributed Thompson Sampling,
we propose a distributed Elimination based Thompson Sampling algorithm that
allow the agents to learn collaboratively. We analyse the algorithm under
Bernoulli reward and derived a problem dependent upper bound on the cumulative
regret.
