Existing multi-agent coordination techniques are often fragile and vulnerable to anomalies such as agent attrition and communication disturbances, which are quite common in the real-world deployment of systems like field robotics. To better prepare these systems for the real world, we present a graph neural network (GNN)-based multi-agent reinforcement learning (MARL) method for resilient distributed coordination of a multi-robot system. Our method, Multi-Agent Graph Embedding-based Coordination (MAGEC), is trained using multi-agent proximal policy optimization (PPO) and enables distributed coordination around global objectives under agent attrition, partial observability, and limited or disturbed communications. We use a multi-robot patrolling scenario to demonstrate our MAGEC method in a ROS 2-based simulator and then compare its performance with prior coordination approaches. Results demonstrate that MAGEC outperforms existing methods in several experiments involving agent attrition and communication disturbance, and provides competitive results in scenarios without such anomalies.

翻译：现有的大规模智能体协同技术往往脆弱，难以应对智能体损耗与通信干扰等异常情况——这些问题在野外机器人等实际系统部署中极为常见。为提升这些系统的实际应用能力，我们提出了一种基于图神经网络（GNN）的多智能体强化学习（MARL）方法，用于实现多机器人系统的弹性分布式协同。该方法名为多智能体图嵌入协同（MAGEC），采用多智能体近端策略优化（PPO）进行训练，能够在智能体损耗、部分可观测性以及有限或受扰通信条件下，实现围绕全局目标的分布式协同。我们通过多机器人巡逻场景，在基于ROS 2的仿真器中演示了MAGEC方法，并与其先前的协同方法进行了性能对比。实验结果表明，在涉及智能体损耗与通信干扰的多项测试中，MAGEC相较于现有方法表现更优；而在无此类异常的场景中，其性能同样具有竞争力。