Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
It addresses the problem of excessive communication bandwidth usage in networked multi-agent systems without requiring accurate system models.
The paper proposes a model-free, priority-driven reinforcement learning algorithm for decentralized multi-agent systems that jointly learns communication priorities and control policies, outperforming baseline methods on benchmark tasks.
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.