Autonomous Communication Decision Making Based on Graph Convolution Neural Network

As a method of multi-agent system cooperation, multi-agent communication can help agents negotiate and adjust behavior decisions by exchanging information such as observation, intention, or experience during operation, improve the overall learning performance, and achieve their learning objectives. However, there are still some challenging problems in multi-agent communication. With the expansion of the multi-agent system scale, the global complete massive information will bring great resource overhead, and the introduction of redundant communication will lead to the difficulty of agent policy convergence, and affect the joint action and target completion. In addition, predefined communication structures have potential cooperation limitations in dynamic environments. In this paper, we introduce a dynamic communication model based on the graph convolution neural network called DCGN. Empirically, we show that DCGN can better cope with the dynamic update of tasks in the process of helping agents complete task information interaction, and can formulate more coordinated strategies than the existing methods.