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Recently, there has been extensive study of cooperative multi-agentmulti-armed bandits where a set of distributed agents cooperatively play thesame multi-armed bandit game. The goal is to develop bandit algorithms with theoptimal group and individual regrets and low communication between agents. Theprior work tackled this problem using two paradigms: leader-follower and fullydistributed algorithms. Prior algorithms in both paradigms achieve the optimalgroup regret. The leader-follower algorithms achieve constant communicationcosts but fail to achieve optimal individual regrets. The state-of-the-artfully distributed algorithms achieve optimal individual regrets but fail toachieve constant communication costs. This paper presents a simple yeteffective communication policy and integrates it into a learning algorithm forcooperative bandits. Our algorithm achieves the best of both paradigms: optimalindividual regret and constant communication costs.

Cooperative Multi-agent Bandits: Distributed Algorithms with Optimal Individual Regret and Constant Communication Costs