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. By learning communication priorities, we circumvent the hybrid action space typical in event-triggered control with binary communication decisions. We evaluate our algorithm on benchmark tasks and demonstrate that it outperforms the baseline method.
@article{arxiv.2605.10482,
title = {Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning},
author = {Qingyun Guo and Junyi Shi and Tomasz Piotr Kucner and Dominik Baumann},
journal= {arXiv preprint arXiv:2605.10482},
year = {2026}
}