Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning

ArXi:2605.10482v1 Announce Type: cross Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems. However, most existing methods rely on accurate system models, which may be unavailable in practice. In this work, we propose a model-free, priority-driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data in decentralized multi-agent systems.