论文标题
多代理深入的强化学习，以在城市空气流动中有效的乘客交付
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
论文作者
论文摘要
人们认为，城市空气流动性（UAM），也称为无人机 - 塔克西或垂直起飞和降落（EVTOL），将在未来的运输中发挥关键作用。通过将UAM置于实际的未来运输中，可以实现几种好处，即（i）与传统运输相比，乘客的总旅行时间可以减少，并且（ii）没有环境污染，也没有特殊的劳动力来操作该系统，因为电池将在UAM系统中使用。但是，在飞行环境中有各种动态和不确定的因素，即乘客突然的服务请求，电池电池排放和UAM之间的碰撞。因此，本文提出了一种基于集中式培训和分布式执行（CTDE）概念的新型合作MADRL算法，以在UAM网络中可靠，有效的乘客交付。根据绩效评估结果，我们确认所提出的算法在服务乘客的数量增加（30％）和每位服务乘客的等待时间下降（26％）方面胜过其他现有算法。
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 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%).
