###### Abstract
How to reasonably formulate task offloading and resource allocation strategies to achieve the goal of balancing task processing delay and energy consumption of unmanned aerial vehicles is a challenging problem. This paper investigates task offloading and resource allocation in multi-UAV MEC systems, aiming to minimize the weighted sum of task processing latency and UAV energy consumption. Given the non-convex nature of this problem and the inclusion of discrete variables, it is modeled as a Markov Decision Process (MDP). A MATD3-based task offloading and resource allocation algorithm is proposed, jointly optimizing MD selection, UAV mobility control, UAV CPU frequency adjustment, and task offloading allocation. This algorithm accelerates convergence, effectively reducing UAV energy consumption and task processing latency. The contributions of this paper are summarized as follows:  
1. A comprehensive task offloading and resource allocation framework for multi-UAV mobile edge computing systems is proposed, addressing scenarios with sparse MD distributions. The framework integrates three key components: (i) a mobility model capturing UAV and MD dynamics, (ii) a communication model accounting for time-varying channel conditions, and (iii) a computational offloading model with resource constraints. This integrated approach enables the formulation of a joint optimization problem that minimizes the weighted sum of task processing latency and UAV energy consumption. 2. By modeling the problem as a Markov Decision Process, a Multi-Agent Twin Delayed Deep Deterministic Policy Gradient for Task Offloading and Resource Allocation algorithm, namely, MATD3-TORA, is proposed to solve the optimization problem. The proposed solution incorporates three key innovations: (i) a twin-critic architecture to prevent overestimation bias, (ii) a delayed policy update mechanism for training stability, and (iii) a distributed execution framework that enables scalable coordination among multiple UAVs. 3. Extensive experiments demonstrate that the proposed MATD3-TORA algorithm achieved superior performance compared to benchmark strategies on the weighted latency and energy consumption. The convergence performance of the proposed algorithm was analyzed under varying learning rates and exploration rates. Furthermore, the algorithm achieved the lowest weighted sum of task processing latency and UAV energy consumption across different computational task volumes and transmission bandwidths, validating its effectiveness. The structure of this paper is organized as follows. Section 2 provides a comprehensive literature review, focusing on the application of deep reinforcement learning in UAV-enabled mobile edge computing systems. Section 3 presents the system models, including the mobility model, communication model, and computational model, which collectively form the foundation for the proposed framework. Section 4 elaborates on the novel Multi-Agent Twin Delayed Deep Deterministic Policy Gradient algorithm. Section 5 provides an in-depth analysis of the experimental results, including a discussion of the algorithm's effectiveness in optimizing task offloading and resource allocation. Finally, Section 6 concludes the paper by summarizing the key findings, contributions, and potential future research directions.