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Safety-Aware Multi-Agent Learning for Dynamic Network Bridging (2404.01551v2)
Published 2 Apr 2024 in cs.MA, cs.AI, cs.LG, cs.NI, cs.SY, and eess.SY
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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