## 1 Introduction  
Currently, the continuous development of IoT technology and 5G mobile technology has promoted the widespread application of augmented reality (AR), virtual reality (VR), vehicle networking, smart cities, and other applications, which require low latency, high efficiency, and network reliability. However, smart mobile devices themselves have limited central processing units, memory, storage, and computational resources. When faced with these computationally intensive applications, smart mobile devices are unable to process them promptly and effectively, and the high power consumption of these applications also reduces the quality of service (QoS) and quality of experience (QoE). Mobile edge computing (MEC) has been proposed as a new computing paradigm. By deploying infrastructure with computing capabilities near smart devices, the tasks generated by smart devices can be transferred to these facilities, shortening the distance between mobile devices and servers, thereby reducing task processing latency. It can be seen that MEC can satisfy the low-latency requirements of a large number of terminal devices for offloading applications [1]. In the MEC environment, IoT devices or mobile users are allowed to offload their tasks to nearby edge servers to overcome the limitations of local computing resources and battery energy. However, under normal circumstances, the deployment of MEC servers is fixed, which means that these edge servers cannot utilize mobility to approach user devices to further reduce device latency and energy consumption. UAV-assisted communication, as a supplement to ground communication, has attracted increasing attention from academia and industry. By integrating unmanned aerial vehicles (UAVs) into MEC networks, UAV-supported MEC architectures have been proposed, in which UAVs can be considered as user devices with computation tasks to execute, as relays to help user devices offload computation tasks, or as MEC servers to perform computation tasks [2; 3; 4]. In summary, due to their flexible deployment, extensive coverage, and reliable wireless communication, UAVs have been widely applied in MEC systems, and by establishing LoS links with ground terminals, UAVs can serve as \"flying MEC servers\" to provide a considerable amount of task offloading services. Previous research focusing on UAV-assisted networks predominantly concentrated on communication aspects [5; 6], but some studies have addressed UAV-assisted MEC systems, covering topics such as trajectory design [7; 8; 9], resource management [10; 11; 12], and computation offloading [13; 14; 15]. Due to the limited computing resources and sensitive battery consumption constraints of single UAVs, most research considers scenarios involving multiple UAVs. In practical computations, UAVs need to provide service to mobile user devices within a designated area while constantly moving, and different UAV trajectories may lead to varying channel quality, resulting in different communication delays and energy consumption. With limited computational resources, the allocation of computational tasks to UAVs also affects computational latency and energy consumption. Moreover, the rapid consumption of UAV battery power impacts overall system performance. Therefore, it is necessary to consider scenarios with multi-UAV and multi-edge cloud collaboration and to minimize execution latency and energy consumption in the joint optimization problem of flight trajectory and computational task allocation. In recent years, some studies have attempted to address the joint optimization problem in UAV-assisted MEC systems through reinforcement learning. In reference [16], research explored UAV-assisted MEC systems by optimizing the trajectories of aerial UAV base stations and access control of mobile user devices. An algorithm called the Aerial-Ground MADDPG (AG-MADDPG) algorithm was proposed to find the optimal joint optimization strategy.