## 2 Literature Review
(2018) addressed the problem of minimizing system energy consumption under inter-cell and intra-cell interference, formulating it as a long-term dynamic optimization problem and decomposing it into CPU scheduling and wireless resource allocation subproblems using Lyapunov stochastic optimization, yet their long-term dynamic formulation may not suit real-time applications. [PERSON] et al. [PERSON] et al. (2018) employed Q-learning and DDQN for joint offloading and resource allocation, but their model suffers from high training complexity in large state spaces. In the context of latency and energy optimization in multi-UAV MEC systems, [PERSON] et al. [PERSON] et al. (2018) studied task offloading in a system comprising high-altitude and low-altitude UAVs, where high-altitude UAVs optimized pricing to maximize revenue, while low-altitude UAVs minimized latency through task offloading. The authors proposed a multi-leader, multi-follower Stackelberg strategy to address this problem. [PERSON] et al. [PERSON] et al. (2018) focused on minimizing system energy consumption in multi-UAV MEC systems, introducing a low-complexity iterative algorithm that jointly optimized user association, energy control, capacity resource allocation, and UAV positioning. This algorithm solved three subproblems and applied fuzzy c-means clustering to obtain feasible solutions. [PERSON] et al. [PERSON] et al. (2018) investigated the minimization of response latency in a multi-UAV MEC system consisting of a central top UAV and distributed bottom UAVs, employing stochastic geometric analysis of the three-dimensional UAV network and queuing theory to solve the problem. [PERSON] et al. [PERSON] et al. (2018) addressed the problem of minimizing total energy consumption during task execution in multi-UAV MEC systems, proposing a block successive upper-bound minimization algorithm to jointly optimize offloading decisions, resource allocation mechanisms, and UAV trajectories. [PERSON] et al. [PERSON] et al. (2018) tackled the minimization of UAV service latency in multi-UAV MEC systems, introducing a multi-layer edge cloud computing task offloading scheme based on integer linear programming. [PERSON] et al. studied the minimization of total system latency in a multi-UAV MEC system integrated with Multiple-Input Multiple-Output (MIMO) technology, employing sequential convex optimization and block coordinate descent techniques to jointly optimize UAV trajectories and data offloading strategies. Recent advancements in UAV-assisted edge computing have introduced innovative reinforcement learning approaches to optimize resource allocation and energy efficiency. For instance, [PERSON] et al. [29] developed a triple-learner-based reinforcement learning framework to simultaneously optimize UAV application placement and energy renewal. Addressing latency minimization, [PERSON] et al. [30] employed DQN and DDPG algorithms to refine both UAV trajectories and virtual machine configurations. [PERSON] et al. [31] introduced an online edge processing scheduling algorithm that dynamically adjusts task processing decisions based on real-time data rates to enhance computational efficiency. To address resource constraints, [PERSON] et al. [32] integrated solar energy harvesting to power UAVs while providing computing services, thereby minimizing overall service costs. For high-dimensional continuous action spaces, [PERSON] et al. [33] leveraged a twin delayed deep deterministic policy gradient (TD3 PG) algorithm to jointly optimize trajectory design, task allocation, and energy management. In the context of vehicular networks, [PERSON] and [PERSON] [34] utilized the MADDPG algorithm to manage multi-dimensional resources, maximizing the number of offloaded tasks. Similarly, [PERSON] et al. [35] focused on energy efficiency, jointly optimizing UAV trajectories and computation offloading decisions while ensuring service fairness and collision avoidance. [PERSON] et al.