Network-level aircraft trajectory planning via multi-agent deep reinforcement learning: Balancing climate considerations and operational manageability

Optimizing flight trajectories emerges as a viable strategy to mitigate the non-CO2 climate impacts of aviation. However, integrating individually optimized trajectories into the air traffic management system poses operational challenges, notably in terms of traffic safety and complexity. This paper...

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Vydané v:	Expert systems with applications Ročník 271; s. 126604
Hlavní autori:	Baneshi, Fateme, Cerezo-Magaña, María, Soler, Manuel
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2025
Predmet:	Air traffic management system Aircraft trajectory planning Aviation climate impact Conflict resolution Multi-agent deep reinforcement learning Twin delayed deep deterministic policy gradient algorithm Multi-agent deep reinforcement learning Air traffic management system Aviation climate impact Aircraft trajectory planning Conflict resolution Twin delayed deep deterministic policy gradient algorithm
ISSN:	0957-4174
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