## 2 Related Work
They proposed a multi-agent reinforcement learning approach for determining a close-optimum offloading policy of bandwidth and task allocation to minimize the computing missions' average response time while taking the dynamic nature of the environment and the UAVs' energy constraints into account. A MDP formulation has also been adopted, in which the policy of offloading is used as the action, and the reward is tied to response time performance. Three distinct topologies for task interdependencies in complex missions are investigated as well as the impact on the offloading policy. In their experimental work, the authors demonstrated a good convergence ability and a significant reduction in response time of complex missions. Considering the scenario of large-scale sparely distributed user equipment, [PERSON] et al. [25] proposed a collaborative framework based on multi-agent deep learning to jointly determine the UAVs' trajectories, communication resource management of UAVs and computation task allocation in a way that minimizes the total of execution delays and energy consumptions in multi-UAVs MEC systems. Furthermore, to deal with the high dimensional action space and find the best offloading policy, a multi-agent twin delayed deep deterministic policy gradient algorithm is used. The framework's implementation resulted in results that show a significant reduction in total system cost when compared to other approaches in both fixed and mobile user equipment scenarios. Multiagent reinforcement learning was also employed in [26] to assist in decision making regarding task offloading from UAVs to edge clouds while minimizing the overall latency perceived by the user and UAV energy consumption. The distributed architecture presented in this paper made it possible to achieve the two aforementioned goals. [PERSON] et al. [27] emphasized the importance of fully utilizing the UAVs computation capabilities for effective remote edge computing in a multi-UAV MEC context. To that end, the authors proposed a multi-hop task offloading scheme with on-the-fly computation in which UAVs perform multi-hop task offloading collaboratively. Furthermore, UAVs use their local processors to perform some tasks. To jointly optimize deployment strategies and resource allocation, two distributed algorithms with linear complexity have been proposed. In the experimental study, both special cases and general cases are considered, and simulation results indicate that better performance in terms of the overall rate of a multi-UAV network has been improved through applying the proposed framework compared to the baseline scheme. In [28], a two-layer optimization strategy is proposed to jointly optimize UAV task scheduling at the first layer via a dynamic programming-based bidding optimization method and bit allocation with UAVs trajectories while resolving the potential path conflict problem. According to simulation results, the proposed strategy outperforms greedy and random strategies in terms of total energy consumption by the user. The proposed system was able to eliminate UAV trajectory conflicts while satisfying safety constraints. Meanwhile, [PERSON] et al. [29] have addressed the load-balancing problem for multi-UAV-aided mobile-edge computing. Specifically, UAVs are used as MEC nodes to provide the computation and storage capabilities for IoT devices. In addition, a new differential evolution (DE)-based technique is proposed to redeploy the UAVs to the best location according to the IoT distributions. Moreover, a deep learning-based approach is designed to solve the task-scheduling problem at each UAV and thereby improve task performance. However, the approach in [29] assumed that UAVs have large enough computation capabilities to address IoT devices, which is not true in the real world, especially, in a large-scale environment. Consequently, this impedes the capability of the system to effectively address real-time applications when the number of IoT devices increases.