Learning Structured Communication for Multi-Agent Reinforcement Learning JAAMAS Track

This paper investigates multi-agent reinforcement learning (MARL) communication mechanisms in large-scale scenarios. We propose a novel framework, Learning Structured Communication (LSC), that leverages a flexible and efficient communication topology. LSC enables adaptive agent grouping to create diverse hierarchical formations over episodes generated through an auxiliary task and a hierarchical routing protocol. We learn a hierarchical graph neural network with the formed topology that facilitates effective message generation and propagation between inter- and intra-group communications. Unlike state-of-the-art communication mechanisms, LSC possesses a detailed and learnable design for hierarchical communication. Numerical experiments on challenging tasks demonstrate that the proposed LSC exhibits high communication efficiency and global cooperation capability.