Scalable Communication for Mobile Multi-Agent Cooperative Detection
Hongye Gao, Tianlong Zhou, Weixiong Rao, Feng Ye · 2023
Communication is an effective mechanism to coordinate the behavior of mobile multi-agent systems. We propose a general mobile multi-agent cooperative detection framework, which provides a detection system with enhanced collaboration capabilities based on graph neural networks and reinforcement learning. Each agent gains information about the surrounding area by heuristic KNN, and then exchanges this part of subgraph information through communication so as to obtain global information in a decentralized way. At the same time, to avoid the computation overhead caused by the increase of the number of agents, attention mechanism is used to filter and optimize the communication process to improve scalability. We evaluate our method on large-scale detection tasks. Our approach is able to outperform the baselines, while making superior communication efficiency1.