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

• URL: http://arxiv.org/abs/2605.10482v2
• Date: Tue, 12 May 2026 07:11:47 GMT
• Title: Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
• Abstract summary: Event-triggered control provides a mechanism for avoiding excessive use of constrained communication bandwidth in networked multi-agent systems.<n>We propose a model-free, priority-driven reinforcement learning algorithm that learns communication priorities and control policies jointly from data.
• Score: 2.182474734552575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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