Decentralized learning enables fixed-wing aircraft to conform to AAM corridor boundaries over 94% of the time and reach goals efficiently in simulated single, sequential, and splitting corridor scenarios with infrequent tactical interventions except at high density.
Hybrid transformer based multi - agent reinforcement learning for multiple unpiloted aerial vehicle coor dination in air corridors,
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A transformer-based soft actor-critic RL policy guides overlapping coalition updates in a potential game for heterogeneous AAV task allocation, yielding 39.76% lower generalized logistics cost than heuristic baselines in 32-AAV/80-task simulations.
citing papers explorer
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Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
Decentralized learning enables fixed-wing aircraft to conform to AAM corridor boundaries over 94% of the time and reach goals efficiently in simulated single, sequential, and splitting corridor scenarios with infrequent tactical interventions except at high density.