Multi-Agent Reinforcement Learning with Control-Theoretic Safety
  Guarantees for Dynamic Network Bridging
Raffaele Galliera Konstantinos Mitsopoulos Niranjan Suri Raffaele Romagnoli
Addressing complex cooperative tasks in safety-critical environments poses
significant challenges for Multi-Agent Systems, especially under conditions of
partial observability. This work introduces a hybrid approach that integrates
Multi-Agent Reinforcement Learning with control-theoretic methods to ensure
safe and efficient distributed strategies. Our contributions include a novel
setpoint update algorithm that dynamically adjusts agents' positions to
preserve safety conditions without compromising the mission's objectives.
Through experimental validation, we demonstrate significant advantages over
conventional MARL strategies, achieving comparable task performance with zero
safety violations. Our findings indicate that integrating safe control with
learning approaches not only enhances safety compliance but also achieves good
performance in mission objectives.