Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162775
Title: Multi-agent deep reinforcement learning for mix-mode runway sequencing
Authors: Shi, Limin
Pham, Duc-Thinh 
Alam, Sameer 
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Issue Date: 2022
Source: Shi, L., Pham, D. & Alam, S. (2022). Multi-agent deep reinforcement learning for mix-mode runway sequencing. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 586-593. https://dx.doi.org/10.1109/ITSC55140.2022.9922221
Abstract: In mixed-mode operation, arrivals and departures are allowed to land and depart on the same runway. An appropriate strategy from air traffic controllers for arrivals and departures sequencing would boost the runway throughput significantly. On the other hand, safety is still the most crucial feature in the operation. Therefore, to assist air traffic controllers to make decisions on departures and arrivals with efficient utilization of runway capacity and safe operations, this paper proposed a Multi-agent Deep Reinforcement Learning approach using Multi-agent Deep Deterministic Policy Gradient to train two agents simultaneously: departure agent, and arrival agent. The departure agent makes departure slotting decisions for departures while the arrival agent determines the time delay or spacing decision on the arrival stream. A data-driven simulation environment is developed using Singapore Changi Airport data to support the learning process. Besides, a random sampling technique is also introduced to reduce redundant samples and increase off-policy sample efficiency. Moreover, the impact of different reward functions on runway throughput is also investigated and two specific models, e.g., 'arrival priority' and 'departure priority', are selected for further analysis in this study. As the result, by comparing the trained models with the ad-hoc model, the proposed approach increases the runway throughput significantly, with the highest 12.8% additional departure releases and overall 5.3% additional departure releases in identical environments while safety separations are maintained.
URI: https://hdl.handle.net/10356/162775
DOI: 10.1109/ITSC55140.2022.9922221
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ITSC55140.2022.9922221.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Conference Papers
MAE Conference Papers

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