Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation

• URL: http://arxiv.org/abs/2305.06446v3
• Date: Sat, 24 Jun 2023 04:36:01 GMT
• Title: Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
• Authors: Yifei Min, Jiafan He, Tianhao Wang, Quanquan Gu
• Abstract summary: We study multi-agent reinforcement learning in the setting of episodic Markov decision processes. We propose a provably efficient algorithm based on value that enable asynchronous communication. We show that a minimal $Omega(dM)$ communication complexity is required to improve the performance through collaboration.
• Score: 77.09836892653176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server. We propose a provably efficient algorithm based on value iteration that enable asynchronous communication while ensuring the advantage of cooperation with low communication overhead. With linear function approximation, we prove that our algorithm enjoys an $\tilde{\mathcal{O}}(d^{3/2}H^2\sqrt{K})$ regret with $\tilde{\mathcal{O}}(dHM^2)$ communication complexity, where $d$ is the feature dimension, $H$ is the horizon length, $M$ is the total number of agents, and $K$ is the total number of episodes. We also provide a lower bound showing that a minimal $\Omega(dM)$ communication complexity is required to improve the performance through collaboration.

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