## 4 Numerical Results
### Algorithm Performance and Robustness Analysis
#### 4.2.3 Detailed Performance Metrics Comparison  
System Efficiency: Figure 8a-c illustrate the system efficiency as a function of the number of UAVs, task computation load, and number of vehicles, respectively. Figure 8a shows the system efficiency as the number of UAVs \(|U|\) changes. As the number of UAVs increases, system efficiency generally improves because more UAVs provide increased computation and caching resources. MAPPO achieves the highest system efficiency across  
Figure 8: System efficiency with varying parameters. (**a**) System efficiency vs. number of UAVs (**U**). (**b**) System efficiency vs. task computation load (Mcycles). (**c**) System efficiency vs. number of vehicles (N). Figure 7: MAPPO performance with different UAV counts. different numbers of UAVs, and its advantage over other algorithms further widens as the number of UAVs increases. This validates MAPPO's ability to effectively coordinate multiple UAV resources and enhance overall system performance. Figure 8b shows the system efficiency as the task computation load changes. A higher task computation load generally leads to decreased system efficiency, as limited computation resources struggle to handle heavy tasks. However, MAPPO consistently maintains the highest system efficiency under various computation loads. Especially under high load, its performance degradation is less significant than other algorithms, demonstrating good robustness. Figure 8c shows the system efficiency as the number of vehicles \(|N|\) changes. Similar to the cache hit rate, the system efficiency shows an initial increase followed by a decrease with the number of vehicles, reflecting the utilization efficiency of system resources under different user scales. MAPPO consistently outperforms other algorithms and peaks when the number of vehicles is around 80-100, indicating its ability to effectively balance user demand and system resources to achieve optimal system efficiency. These results consistently demonstrate that the proposed MAPPO algorithm significantly improves system efficiency across different system scales and load conditions, outperforming other baseline strategies. Cache Hit Rate: Figure 9 compares the cache hit rate of different strategies as the number of vehicles changes. As shown in Figure 9, the cache hit rate shows a trend of initially increasing and then decreasing as the number of vehicles increases. This is because when the number of vehicles is small, the cache capacity is relatively sufficient, leading to a high hit rate. As the number of vehicles increases beyond a certain point, the limited cache capacity cannot satisfy all requests, causing the hit rate to decline. Across the entire range of vehicle numbers, the MAPPO algorithm consistently maintains the highest cache hit rate. Particularly when the number of vehicles is between 80 and 120, the advantage of MAPPO is more pronounced, reaching a hit rate of around 82%. The cache hit rates of MADDPG and MAAC are close but lower than MAPPO. The Greedy and Random policies exhibit the lowest cache hit rates, indicating their inefficiency in content caching decisions. This demonstrates the effectiveness of MAPPO in learning dynamic caching strategies to maximize the hit rate. Total Delay: Figure 10 compares the total task delay of different strategies as computing power (GHz) changes. (Note: The x-axis is labeled \"Computing Power (GHz)\". Given the trend of decreasing delay as this value increases, it should be interpreted as the effective computing capacity available to the system or relevant to task computation.) As shown in Figure 10, with the increase in computing power (i.e., the value on the x-axis increases), the total task delay of all strategies decreases significantly. This aligns with intuition, as more computing resources can process tasks faster. Across all levels of computing power, the total delay of the MAPPO algorithm is consistently the lowest.