Safety-Aware Multi-Agent Learning for Dynamic Network Bridging

Raffaele Galliera; Konstantinos Mitsopoulos; Niranjan Suri; Raffaele Romagnoli

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arXiv · 2404.01551

Safety-Aware Multi-Agent Learning for Dynamic Network Bridging

Raffaele Galliera·Konstantinos Mitsopoulos·Niranjan Suri·Raffaele Romagnoli

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Abstract

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.

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Raffaele Galliera, Konstantinos Mitsopoulos, Niranjan Suri, Raffaele Romagnoli. 2024-04-02. Safety-Aware Multi-Agent Learning for Dynamic Network Bridging. https://arxiv.org/abs/2404.01551

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