mlsanityData to 14 Oct 2026

Safety-Aware Multi-Agent Learning for Dynamic Network Bridging

arXiv 2404.01551 ↗First posted 2 Apr 2024cs.MA cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 2 Apr 2024.

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9 of 19 references.

References · 9

  1. 2017Graph Attention Networkscited by 8,923
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
  4. 2013The Complexity of Decentralized Control of Markov Decision Processescited by 231
  5. 2023The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systemscited by 88
  6. 2021Safe Multi-Agent Reinforcement Learning via Shieldingcited by 61
  7. 2022Provably Safe Reinforcement Learning via Action Projection using Reachability Analysis and Polynomial Zonotopescited by 26
  8. 2021Safe Reinforcement Learning Using Robust Action Governorcited by 14
  9. 2023Online Control Barrier Functions for Decentralized Multi-Agent Navigationcited by 11

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  2. 2026Safe Heterogeneous Multi-Agent RL with Communication Regularization for Coordinated Target Acquisition
  3. 2026Decentralized Safe Multi-Agent Reinforcement Learning via Predictive Shielding
  4. 2026Contract-Based Compositional Shielding for Safe Multi-Agent Reinforcement Learning
  5. 2026Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
  6. 2025Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
  7. 2025Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning
  8. 2025Tackling Uncertainties in Multi-Agent Reinforcement Learning through Integration of Agent Termination Dynamics