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Social Learning in Multi Agent Multi Armed Bandits

We introduce a novel decentralized, multi agent version of the classical Multi-Arm Bandit (MAB) problem, consisting of n agents, that collaboratively and simultaneously solve the same instance of K armed MAB to minimize individual regret. The agents can communicate and collaborate among each other only through a pairwise asynchronous gossip based protocol that exchange a limited number of bits. In our model, agents at each point decide on (i) which arm to play, (ii) whether to, and if so (iii) what and whom to communicate with.