Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160172
Title: A multi-agent reinforcement learning approach for system-level flight delay absorption
Authors: Malhotra, Kanupriya
Lim, Zhi Jun
Alam, Sameer
Keywords: Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Aeronautical engineering::Air navigation
Issue Date: 2022
Source: Malhotra, K., Lim, Z. J. & Alam, S. (2022). A multi-agent reinforcement learning approach for system-level flight delay absorption. 2022 Winter Simulation Conference.
Abstract: 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.
URI: https://hdl.handle.net/10356/160172
URL: https://dl.acm.org/conference/wsc
Rights: © 2022 Winter Simulation Conference. All rights reserved. This paper was published in Proceedings of the 2022 Winter Simulation Conference and is made available with permission of Winter Simulation Conference.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers

Files in This Item:
File Description SizeFormat 
A_Multi_Agent_Reinforcement_Learning_Approach_for_E_AMAN (1).pdf3.28 MBAdobe PDFThumbnail
View/Open

Page view(s)

40
Updated on Jan 27, 2023

Download(s)

3
Updated on Jan 27, 2023

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.