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Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobilityoa mark
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Publication Year
2023-01-01
Journal
IEEE International Conference on Communications
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE International Conference on Communications, Vol.2023-May, pp.5689-5694
Keyword
Air transportation serviceMulti-agent deep reinforcement learning (MADRL)Urban Air Mobility (UAM)
Mesh Keyword
Air transportation serviceEnvironmental pollutionsMulti agentMulti-agent deep reinforcement learningReinforcement learningsTransportation servicesTravel-timeUrban airUrban air mobilityVertical take-off and landings
All Science Classification Codes (ASJC)
Computer Networks and CommunicationsElectrical and Electronic Engineering
Abstract
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation. By putting UAM into practical future transportation, several benefits can be realized, i.e., (i) the total travel time of passengers can be reduced compared to traditional transportation and (ii) there is no environmental pollution and no special labor costs to operate the system because electric batteries will be used in UAM system. However, there are various dynamic and uncertain factors in the flight environment, i.e., passenger sudden service requests, battery discharge, and collision among UAMs. Therefore, this paper proposes a novel cooperative multiagent deep reinforcement learning (MADRL) algorithm based on centralized training and distributed execution (CTDE) concepts for reliable and efficient passenger delivery in UAM networks. According to the performance evaluation results, we confirm that the proposed algorithm outperforms other existing algorithms in terms of the number of serviced passengers increase (30%) and the waiting time per serviced passenger decrease (26% ).
ISSN
1550-3607
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36947
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85174473843&origin=inward
DOI
https://doi.org/10.1109/icc45041.2023.10279436
Type
Conference
Funding
This research was funded by National Research Foundation of Korea (2022R1A2C2004869, 2021R1A4A1030775). (Corresponding authors: Soyi Jung, Joongheon Kim).
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Jung, Soyi Image
Jung, Soyi정소이
Department of Electrical and Computer Engineering
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