The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this paper proposes a novel air transportation service management algorithm based on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM cooperation. Specifically, the proposed algorithm in this paper is based on communication network (CommNet) method utilizing centralized training and distributed execution (CTDE) in multiple UAMs for providing efficient air transportation services to passengers collaboratively. Furthermore, this paper adopts actual vertiport maps and UAM specifications for constructing realistic air transportation networks. By evaluating the performance of the proposed algorithm in data-intensive simulations, the results show that the proposed algorithm outperforms existing approaches in terms of air transportation service quality. Furthermore, there are no inferior UAMs by utilizing parameter sharing in CommNet and a centralized critic network in CTDE. Therefore, it can be confirmed that the research results in this paper can provide a promising solution for autonomous air transportation management systems in city-wide urban areas.

翻译：城市空中交通（UAM）的发展正迅速推进，由于多方面的环境不确定性，对高效交通管理系统的需求日益增长。为此，本文提出一种基于多智能体深度强化学习（MADRL）的新型空中交通服务管理算法，以应对多UAM协同合作的挑战。具体而言，本文算法基于通信网络（CommNet）方法，采用集中式训练与分布式执行（CTDE）策略，使多个UAM能够协同为乘客提供高效的空中交通服务。此外，本文采用实际的垂直起降机场地图和UAM参数构建真实的空中交通网络。通过在数据密集型仿真中评估所提算法的性能，结果表明，该算法在空运服务质量方面优于现有方法。同时，由于在CommNet中采用参数共享及在CTDE中使用集中式评判网络，不存在性能较差的UAM。因此，可以确认本文的研究成果可为城市范围的自主空中交通管理系统提供一种有前景的解决方案。