## 4 Numerical Results
### Algorithm Performance and Robustness Analysis
#### 4.2.2 Hyperparameter and System Scale Analysis  
To further analyze the robustness and performance of the proposed MAPPO algorithm, we investigate the impact of different hyperparameters (Batch Size, Learning Rate) and system scale (Number of UAVs) on the cumulative reward convergence.  
Figure 6a compares the cumulative reward convergence curves of the MAPPO algorithm under different Batch Size settings. As shown in Figure 6a, the choice of Batch Size has a significant impact on the training effectiveness of MAPPO. With Batch Sizes of 32 and 64, the algorithm converges fastest and achieves the highest final cumulative reward, showing optimal performance. With a Batch Size of 128, the convergence is slightly slower, and the final performance is marginally lower. With a Batch Size of 256, the convergence is significantly slower, and the final cumulative reward is the lowest, indicating the poorest performance among tested values. This suggests that in this simulation environment, smaller Batch Sizes (e.g., 32 or 64) are more conducive to the learning process and performance improvement of the MAPPO algorithm.  
Impact of Learning Rate on Performance: Figure 6b compares the cumulative reward convergence curves of the MAPPO algorithm under different Learning Rate settings. As depicted in Figure 6b, the selection of the Learning Rate is crucial for the algorithm's convergence speed and stability. With a Learning Rate of 0.001, the algorithm converges quickly and stably to a high cumulative reward, exhibiting the best performance. With a Learning Rate of 0.01, although the initial convergence is very fast, it shows fluctuations or premature saturation in the later stages, resulting in a slightly lower final cumulative reward than 0.001. With a Learning Rate of 0.0001, the convergence speed is very slow. While it might eventually reach a reward level similar to 0.001, it requires significantly more training episodes. This highlights that selecting an appropriate Learning Rate (0.001) is vital for balancing convergence speed and training stability.  
Impact of Number of UAVs on Performance (Robustness Analysis): Figure 7 compares the cumulative reward convergence curves of the MAPPO algorithm with different numbers of UAVs. As shown in Figure 7, as the number of UAVs increases from 4 to 16, the final cumulative reward of the MAPPO algorithm shows a clear upward trend. This is consistent with the trend observed in Figure 8a, where system efficiency improves with an increasing number of UAVs. More UAVs provide richer computation, caching, and communication resources, allowing the system to better serve vehicle requests. The MAPPO algorithm is capable of effectively utilizing these additional resources and achieves good learning performance across different scales of UAV deployment, which indicates that the proposed algorithm exhibits a certain degree of robustness to changes in system scale.  
Figure 6: MAPPO algorithm performance with different hyperparameter settings. (**a**) MAPPO performance with different Batch Sizes. (**b**) MAPPO performance with different Learning Rates.