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Multi-Agent Reinforcement Learning with Control-Theoretic Safety Guarantees for Dynamic Network Bridging
Addressing complex cooperative tasks in safety-critical environments poses significant challenges for Multi-Agent Systems, especially under conditions of partial observability.
Reinforcement Learning-based Optimal Control and Software Rejuvenation for Safe and Efficient UAV Navigation
We compare our approach with traditional methods and show that it improves the system's performance while maintaining safety constraints.
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