## 5 Experiments and Analysis
### Algorithm Comparison  
To effectively compare and evaluate the performance of the MATD3-TORA task offloading and resource allocation strategy in multi-UAV MEC systems, this study employed benchmark methods, including MAPPO [46] and QMIX [47]. All reinforcement learning algorithms underwent state normalization, as described below:  
1. Comparison with Edge-only In this approach, the UAVs are stationed at fixed positions at the center of the region, functioning exclusively as Mobile Edge Computing servers for the mobile devices. At each time slot, all computational tasks generated by the mobile devices are offloaded to the UAVs for processing. This method relies entirely on the computational capabilities of the UAVs, with no tasks executed locally on the mobile devices. While this approach leverages the UAVs' processing energy, it fails to optimize resource utilization and may lead to inefficiencies in task distribution. 2. Comparison with Local-only In contrast to the Edge-only method, this approach executes all computational tasks locally on the mobile devices without any assistance from the UAVs. The mobile devices rely solely on their own processing capabilities to complete all tasks. Although this method eliminates the need for task offloading, it is constrained by the limited computational resources of the mobile devices, resulting in higher delays and energy consumption for resource-intensive tasks. 3. Comparison with Random In this method, the UAVs are positioned at fixed locations at the center of the region and randomly select mobile devices and offloading ratios to process computational tasks. The movement of the UAVs and the selection of tasks are entirely random, lacking any optimization or strategic decision making. As a result, this method fails to achieve consistent performance and is highly inefficient in resource allocation. 4. Comparison with MADDPG The MADDPG algorithm, a well-established multi-agent reinforcement learning method, employs a single-target critic network to handle continuous action spaces. While it is suitable for multi-agent environments, it is prone to Q-value overestimation, which can hinder its convergence speed and optimization performance. Despite these limitations, MADDPG serves as a relevant benchmark for comparison due to its applicability to similar problem domains. (5) Performance Analysis of MATD3-TORA  
All reinforcement learning algorithms were trained for a total of 1000 iterations. As shown in the figure, the Random algorithm failed to converge due to its reliance on random selection or generation of solutions rather than deterministic rules or optimization processes. In contrast, both MADDPG and MATD3-TORA achieved convergence, with MATD3-TORA demonstrating superior performance. Specifically, this accelerated convergence of MATD3-TORA can be attributed to its innovative design, which addresses the issue of Q-value overestimation through the use of dual-critic networks, delayed policy updates, and target policy smoothing. These features enable MATD3-TORA to achieve more stable and efficient optimization compared to MADDPG. Furthermore, the MATD3-TORA algorithm significantly outperformed the Edge-only and Local-only methods in terms of weighted delay and energy consumption. Specifically, MATD3-TORA achieved reductions in weighted delay and energy consumption, respectively. These results underscore the superior performance of MATD3-TORA in optimizing task offloading and resource allocation in multi-UAV MEC systems. By intelligently balancing the computational load between UAVs and mobile devices, MATD3-TORA minimizes delays and energy consumption while maximizing system efficiency. In summary, the experimental results demonstrate that MATD3-TORA not only converges faster than MADDPG but also achieves significant performance improvements over traditional methods such as Edge-only and Local-only.