## 5 Simulation Results
### Performance Evaluation  
The average cumulative rewards per episode are shown in Figure 3, where AlexNet, YOLO, and VGG16 were employed as deployed CNN models for the mission. The maximum reward line in Figure 3 is the value that the UAV swarms are optimally distributed within the mission area. The proposed system is unable to achieve the maximum reward due to a penalty resulting from the UAV's positions being initialized by following a Gaussian distribution. However, it was confirmed that as the number of UAVs in the swarm increased, the converged reward improved significantly. This performance improvement is attributed to flexibility of data transmission paths and resource utilization efficiency within the UAV swarm. The optimal solution was achieved by the AlexNet architecture with a slight difference from the maximum reward, a marginal difference was conformed with the YOLO architecture, and a moderate difference was exhibited with the VGG16 architecture. Additional UAVs may be necessary to reach optimal performance in VGG16, as it requires more average computational requirements required by a layer than other deployed CNN models. The limitations observed in the simulations are that as the swarm size increases, the instability in the early stages of learning increases and the model requires more episodes to reach saturation. This phenomenon can be attributed to the increased environmental complexity, which is a consequence of the expanded state and action spaces concomitant with an increased number of UAVs in the swarm. The performance of the proposed model compared to three baselines is shown in Figure 4. The performance of the proposed model is evaluated under the same conditions, including fifteen UAVs, 1 GB memory, and a computational capacity of 11.2 GFLOPS. The Layer-Avg method [24] refers to the method that distributes layers to each UAV as uniformly as possible according to the layers of the CNN model. The Comp-Avg method [24] distributes the computational load assigned to each UAV as uniformly as possible according to the computational load of the CNN layers. The Heuristic method [25] searches the nearest neighboring node with sufficient resources to process the remaining layers during intermediate data transmission. The proposed model outperformed the \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline discount factor & 0.99 \\ actor learning rate & 0.0003 \\ critic learning rate & 0.001 \\ clip range & 0.2 \\ inoperable state parameter & 2 \\ noise power spectral density & \(4\times 10^{-21}\) \\ transmission power & 0.1 W \\ bandwidth & 1 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters of DRL-RLD model. Figure 3: Average cumulative rewards by CNN models: (a) AlexNet; (b) YOLO; (c) VGG16. Layer-Avg and Comp-Avg methods in all three CNN models. The Layer-Avg and Comp-Avg methods focus on evenly distributing the requirements of the CNN layers. However, the Layer-Avg and Comp-Avg methods do not adequately consider the physical distances between UAVs, which significantly impact transmission latency. In contrast, the proposed model achieves superior performance by accounting for transmission latency between UAVs. The heuristic method prioritizes short-term benefits when distributing layers. On the contrary, the proposed system achieves long-term benefits by distributing layers with a comprehensive consideration of available resources and UAV arrangement. Consequently, the proposed model was confirmed to have lower the end-to-end latency than the three baseline methods by thoroughly considering factors such as UAV locations, communication links, and resource constraints during layer distribution. A comparative performance of the proposed system under different memory constraints (1 GB and 2 GB) is presented in Figure 5. Simulations were performed under the same conditions, except for memory constraints, with 15 UAVs and a computational capacity of 11.2 GFLOPS.