Title: Learning Structured Communication for Multi-agent Reinforcement Learning. Abstract: Learning to cooperate is crucial for many practical large-scale multi-agent applications. In this work, we consider an important collaborative task, in which agents learn to efﬁciently communicate with each other under a multi-agent reinforcement learning (MARL) setting. Despite the fact that there has been a number of existing works along this line, achieving global cooperation at scale is still challenging. In particular, most of the existing algorithms suffer from issues such as scalability and high communication complexity, in the sense that when the agent population is large, it can be difﬁcult to extract effective information for high-performance MARL. In contrast, the proposed algorithmic framework, termed Learning Structured Communication (LSC), is not only scalable but also communication high-qualitative (learning efﬁcient). The key idea is to allow the agents to dynamically learn a hierarchical communication structure, while under such a structure the graph neural network (GNN) is used to efﬁciently extract useful information to be exchanged between the neighboring agents. A number of new techniques are proposed to tightly integrate the communication structure learning, GNN optimization and MARL tasks. Extensive experiments are performed to demonstrate that, the proposed LSC framework enjoys high communication efﬁciency, scalability and global cooperation capability.