  This paper proposes a novel policy for a group of agents to, individually as
well as collectively, solve a multi armed bandit (MAB) problem. The policy
relies solely on the information that an agent has obtained through sampling of
the options on its own and through communication with neighbors. The option
selection policy is based on an Upper Confidence Based (UCB) strategy while the
communication strategy that is proposed forces agents to communicate with other
agents who they believe are most likely to be exploring than exploiting. The
overall strategy is shown to significantly outperform an independent
Erd\H{o}s-R\'{e}nyi (ER) graph based random communication policy. The policy is
shown to be cost effective in terms of communication and thus to be easily
scalable to a large network of agents.
