###### Abstract  
The integration of Unmanned Aerial Vehicles (UAVs) into Mobile Edge Computing (MEC) systems has emerged as a transformative solution for latency-sensitive applications, leveraging UAVs' unique advantages in mobility, flexible deployment, and on-demand service provisioning. This paper proposes a novel multi-agent reinforcement learning framework, termed Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation (MATD3-TORA), to optimize task offloading and resource allocation in UAV-assisted MEC networks. The framework enables collaborative decision making among multiple UAVs to efficiently serve sparsely distributed ground mobile devices (MDs) and establish an integrated mobility, communication, and computational offloading model, which formulates a joint optimization problem aimed at minimizing the weighted sum of task processing latency and UAV energy consumption. Extensive experiments demonstrate that the algorithm achieves improvements in system latency and energy efficiency compared to conventional approaches. The results highlight MATD3-TORA's effectiveness in addressing UAV-MEC challenges, including mobility-energy tradeoffs, distributed decision making, and real-time resource allocation. E 29 March 202514 May  
streaming, video surveillance, V2X (Vehicle-to-Everything) applications, and industrial control. These applications aim to reduce network latency and significantly enhance service quality. In 2016, ETSI rebranded MEC as Multi-Access Edge Computing, reflecting its expanded scope from traditional 3 GPP cellular networks to include fixed networks, Wi-Fi, and other access networks. Compared to MCC, MEC deploys small-scale data centers within wireless access networks near users, offering lower latency and higher service quality. This proximity ensures faster mobile data (MD) transmission and mitigates network congestion, thereby improving overall resource utilization. Unmanned aerial vehicles (UAVs) have emerged as a promising platform for MEC, offering unique advantages such as high mobility, flexible deployment, and the ability to provide on-demand computational services in remote or disaster-stricken areas. In single-UAV MEC systems, however, task offloading and resource allocation face challenges due to the limited number and compact distribution of MDs. In scenarios with sparse MD distributions, UAVs are constrained by battery life and computational capacity, making it difficult to provide effective services. To address this, multi-UAV MEC systems have been proposed, where multiple UAVs collaboratively provide computational offloading services to ground MDs. Studies on the tradeoff between latency and energy consumption in multi-UAV MEC systems have focused on weighted system-wide latency and energy consumption [5] or the latency and energy issues in communication and computation processes [6]. Recent advances in energy-aware scheduling for Mobile Edge Computing leverage optimization and machine learning to minimize system energy consumption across diverse scenarios. Key approaches include deep reinforcement learning for asynchronous task offloading [7], game-theoretic and iterative methods for multi-user resource allocation [8; 9], alternating optimization in STAR-RIS-assisted systems [10], Lyapunov-based interference management [11], and Q-learning for dynamic multi-user environments [12]. These studies collectively demonstrate the effectiveness of model-driven and learning-based techniques in optimizing MEC energy efficiency, with reinforcement learning emerging as a particularly promising direction for adaptive resource management in complex edge computing networks. However, these algorithms are limited by resources such as communication, computing and energy, which makes it necessary to balance its own energy consumption while reducing the delay of task processing.