Strategic conflict management using recurrent multi-agent reinforcement learning for urban air mobility operations considering uncertainties

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dc.contributor.authorHuang, Cheng
dc.contributor.authorPetrunin, Ivan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2023-02-01T14:20:26Z
dc.date.available2023-02-01T14:20:26Z
dc.date.issued2023-01-26
dc.description.abstractThe rapidly evolving urban air mobility (UAM) develops the heavy demand for public air transport tasks and poses great challenges to safe and efficient operation in low-altitude urban airspace. In this paper, the operation conflict is managed in the strategic phase with multi-agent reinforcement learning (MARL) in dynamic environments. To enable efficient operation, the aircraft flight performance is integrated into the process of multi-resolution airspace design, trajectory generation, conflict management, and MARL learning. The demand and capacity balancing (DCB) issue, separation conflict, and block unavailability introduced by wind turbulence are resolved by the proposed the multi-agent asynchronous advantage actor-critic (MAA3C) framework, in which the recurrent actor-critic networks allow the automatic action selection between ground delay, speed adjustment, and flight cancellation. The learned parameters in MAA3C are replaced with random values to compare the performance of trained models. Simulated training and test experiments performed on a small urban prototype and various combined use cases suggest the superiority of the MAA3C solution in resolving conflicts with complicated wind fields. And the generalization, scalability, and stability of the model are also demonstrated while applying the model to complex environments.en_UK
dc.identifier.citationHuang C, Petrunin I, Tsourdos A. (2023) Strategic conflict management using recurrent multi-agent reinforcement learning for urban air mobility operations considering uncertainties. Journal of Intelligent and Robotic Systems, Volume 107, February 2023, Article number 20en_UK
dc.identifier.issn0921-0296
dc.identifier.urihttps://doi.org/10.1007/s10846-022-01784-0
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/19083
dc.language.isoenen_UK
dc.publisherSpringeren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectStrategic conflict managementen_UK
dc.subjectMulti-agent reinforcement learningen_UK
dc.subjectUrban air mobilityen_UK
dc.subjectWind turbulenceen_UK
dc.titleStrategic conflict management using recurrent multi-agent reinforcement learning for urban air mobility operations considering uncertaintiesen_UK
dc.typeArticleen_UK

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