Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916003312566272 |
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| author | Guo, Qingyun Shi, Junyi Kucner, Tomasz Piotr Baumann, Dominik |
| author_facet | Guo, Qingyun Shi, Junyi Kucner, Tomasz Piotr Baumann, Dominik |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10482 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning Guo, Qingyun Shi, Junyi Kucner, Tomasz Piotr Baumann, Dominik Systems and Control Machine Learning Robotics 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. |
| title | Priority-Driven Control and Communication in Decentralized Multi-Agent Systems via Reinforcement Learning |
| topic | Systems and Control Machine Learning Robotics |
| url | https://arxiv.org/abs/2605.10482 |