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Reinforcement learning in cooperative multi-agent settings has recentlyadvanced significantly in its scope, with applications in cooperativeestimation for advertising, dynamic treatment regimes, distributed control, andfederated learning. In this paper, we discuss the problem of cooperativemulti-agent RL with function approximation, where a group of agentscommunicates with each other to jointly solve an episodic MDP. We demonstratethat via careful message-passing and cooperative value iteration, it ispossible to achieve near-optimal no-regret learning even with a fixed constantcommunication budget. Next, we demonstrate that even in heterogeneouscooperative settings, it is possible to achieve Pareto-optimal no-regretlearning with limited communication. Our work generalizes several ideas fromthe multi-agent contextual and multi-armed bandit literature to MDPs andreinforcement learning.

Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximation