Integrated frameworks of unsupervised, supervised and reinforcement learning for solving air traffic flow management problem

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dc.contributor.authorHuang, Cheng
dc.contributor.authorXu, Yan
dc.date.accessioned2021-12-15T14:01:25Z
dc.date.available2021-12-15T14:01:25Z
dc.date.issued2021-11-15
dc.description.abstractThis paper studies the demand-capacity balancing (DCB) problem in air traffic flow management (ATFM) with collaborative multi-agent reinforcement learning (MARL). To attempt the proper ground delay for resolving airspace hotspots, a multi-agent asynchronous advantage actor-critic (MAA3C) framework is firstly constructed with the long short-term memory network (LSTM) for the observations, in which the number of agents varies across training steps. The unsupervised learning and supervised learning are then introduced for better collaboration and learning among the agents. Experimental results demonstrate the scalability and generalization of the proposed frameworks, by means of applying the trained models to resolve different simulated and real-world DCB scenarios, with various flights number, sectors number and capacity settings.en_UK
dc.description.sponsorship10.13039/501100004543-China Scholarship Councilen_UK
dc.identifier.citationHuang C, Xu Y. (2021) Integrated frameworks of unsupervised, supervised and reinforcement learning for solving air traffic flow management problem. In: Proceedings of the 2021 AIAA/IEEE 40th Digital Avionics Systems Conference (DASC), 3-7 October 2021, San Antonio, TX, USA.en_UK
dc.identifier.eisbn978-1-6654-3420-1
dc.identifier.eissn2155-7209
dc.identifier.isbn978-1-6654-3421-8
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/DASC52595.2021.9594397
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/17336
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectDCBen_UK
dc.subjectMulti-agenten_UK
dc.subjectReinforcement Learningen_UK
dc.subjectUnsupervised Learningen_UK
dc.subjectSupervised Learningen_UK
dc.titleIntegrated frameworks of unsupervised, supervised and reinforcement learning for solving air traffic flow management problemen_UK
dc.typeConference paperen_UK

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