Separate DQN policies for UAV and eVTOL agents are trained to keep separation in a simulated structured corridor with degraded surveillance, and their behavior is summarized as action shares and Pareto-optimal safety/capacity settings.
Building Public–Private Partnerships for Advanced Air Mobility Infrastructure Using Game Theory,
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Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning
Separate DQN policies for UAV and eVTOL agents are trained to keep separation in a simulated structured corridor with degraded surveillance, and their behavior is summarized as action shares and Pareto-optimal safety/capacity settings.