## 1 Introduction  
Wireless sensors and Internet of Things (IoT) devices are now common and ubiquitous in today's world, and their application and utility have expanded significantly. Furthermore, with the introduction of stable, high-speed internet (e.g., in 5G and 6G), many new services and applications in the fields of virtual/augmented reality, facial recognition, video games and video streaming, e-health, vehicular networks, and natural language processing [1, 2] have emerged. However, these services and applications require increased computing capacity and energy efficiency, which creates new challenges for these devices to address due to their limited computation and battery capacity [3, 4]. As a result, task offloading is proposed as a prominent solution to these limitations, in which intensive and delay-tasks could be offloaded and then executed remotely at more powerful devices [5, 6]. Mobile cloud computing (MCC) is regarded as a valuable solution for offloading the intensive tasks of IoT devices to cloud computing resources [7, 8, 9]. Due to the centralization of cloud computing resources, the implementation of this solution results in high latency. MCC is also confronted with the issue of security [10, 11]. As a result, researchers tend to distribute cloud computing resources to the nearby edge of IoT users, resulting in a new paradigm known as mobile edge computing (MEC), which efficiently addresses latency and security issues [12,13]. On the other hand, due to their constant use in a variety of fields, Unmanned Aerial Vehicle (UAV) systems have seen an increase in popularity and attracted considerable attention in recent years. Furthermore, UAVs' low deployment cost and high mobility make them popular and appealing devices for aided communication networks, acting as relay nodes [14], flying base stations [15], or terminal nodes [16]. Several models and approaches have been proposed recently for using computational offloading with UAV-assisted MEC systems [17]. From one perspective, some models focus on a single objective, while others cover multiple objectives [18]. From another perspective, some approaches address the task offloading for a single edge while others cover for a multiple edge server with and without the cloud [19]. Nevertheless, most of these solutions allow the mobile users to only offload their tasks to stations that are connected, implying an unbalanced load at stations [12,20]. Consequently, it may be difficult for some mobile users to complete their computation tasks within the acceptable latency threshold. Meanwhile, implementing an optimal strategy for multi-user in dynamic and complex systems such as multi-users MEC system is another challenging issue that should be carefully addressed. Motivated by these considerations, we propose an efficient technique for multi-user and multi-edges systems in this paper to balance base station loads and reduce system costs. A novel and efficient offloading algorithm is also designed to find a near-optimal solution. The main contributions reported in this paper are summarized as follows:  
* An efficient load-balancing algorithm is introduced for optimizing the load among GBSs, in which the mobile users are redistributed to the most appropriate GBSs regarding their location, task size, and CPU cycles. In addition, UAVs are utilized as a potential MEC server to provide computation and communication resources by hovering in overcrowded areas where the GBSs server is still overloaded. * Task offloading, load balancing, and resource allocation are jointly optimized for multi-tiers UAV-aided MEC systems via a formulation of an integer programming problem with the primary objective of minimizing system cost. * A novel form of deep reinforcement learning is introduced in which the application task's requirements represent the system state and the offloading decision is used to define the action. The solution is then derived using an efficient distributed deep reinforcement-learning-based algorithm.