Safety-Aware Multi-Agent Learning for Dynamic Network Bridging
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908297559277568 |
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| author | Galliera, Raffaele Mitsopoulos, Konstantinos Suri, Niranjan Romagnoli, Raffaele |
| author_facet | Galliera, Raffaele Mitsopoulos, Konstantinos Suri, Niranjan Romagnoli, Raffaele |
| contents | Addressing complex cooperative tasks in safety-critical environments poses significant challenges for multi-agent systems, especially under conditions of partial observability. We focus on a dynamic network bridging task, where agents must learn to maintain a communication path between two moving targets. To ensure safety during training and deployment, we integrate a control-theoretic safety filter that enforces collision avoidance through local setpoint updates. We develop and evaluate multi-agent reinforcement learning safety-informed message passing, showing that encoding safety filter activations as edge-level features improves coordination. The results suggest that local safety enforcement and decentralized learning can be effectively combined in distributed multi-agent tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01551 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safety-Aware Multi-Agent Learning for Dynamic Network Bridging Galliera, Raffaele Mitsopoulos, Konstantinos Suri, Niranjan Romagnoli, Raffaele Multiagent Systems Artificial Intelligence Machine Learning Networking and Internet Architecture Systems and Control Addressing complex cooperative tasks in safety-critical environments poses significant challenges for multi-agent systems, especially under conditions of partial observability. We focus on a dynamic network bridging task, where agents must learn to maintain a communication path between two moving targets. To ensure safety during training and deployment, we integrate a control-theoretic safety filter that enforces collision avoidance through local setpoint updates. We develop and evaluate multi-agent reinforcement learning safety-informed message passing, showing that encoding safety filter activations as edge-level features improves coordination. The results suggest that local safety enforcement and decentralized learning can be effectively combined in distributed multi-agent tasks. |
| title | Safety-Aware Multi-Agent Learning for Dynamic Network Bridging |
| topic | Multiagent Systems Artificial Intelligence Machine Learning Networking and Internet Architecture Systems and Control |
| url | https://arxiv.org/abs/2404.01551 |