A multi-agent reinforcement learning approach for system-level flight delay absorption - Malhotra, Kanupriya (2023)
With increasing air traffic, there is an ever-growing need for Air Traffic Controllers (ATCO) to efficiently manage traffic and congestion. Congestion often leads to increased delays in the Terminal Maneuvering Area (TMA), causing large amounts of fuel burn and detrimental environmental impacts. Approaches such as the Extended Arrival Manager (E-AMAN) propose solutions to absorb such delays, whereby flights are scheduled much before they enter the TMA. However, such an approach requires a speed management system where flights can coordinate to absorb system-level delays in their en-route phase. This paper proposes a Multi-Agent System (MAS) approach using Deep Reinforcement Learning to model and train flights as agents which can coordinate with each other to effectively absorb system-level delays. The simulations utilize Multi-Agent POsthumous Credit Assignment in Unity and test two reward approaches. Initial findings reveal an average of 3.3 minutes of system-level delay absorptions from a required delay of 4 minutes.