## 4 Solutions
### Algorithm Structure
The key parameters used in the simulations are illustrated in Table 1. To reduce the experimental time, we open 12 parallel environments to simultaneously collect data. After each data collection episode, the algorithm runs 15 training epochs to fully utilize the data. The data size for our small batch training is 512. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Region & 3 km \(\times\) 3 km \\ Number of rescue centers & 10 \\ Drone hight, \(H\) & 16 m \\ Drone number, \(U\) & 3, 4, 5, 6 \\ Moving range of a drone at one time & [0, 300 m] \\ Upper limit of served UE number for each drone & 40 \\ UE height, \(h\) & 1.5 m \\ Number of UEs, \(N\) & 200 \\ Mean additional loss, \(\eta\) & 1 dB \\ Received signal power, \(P_{t}\) & \(-\)3 dBW \\ Carrier frequency, \(f_{c}\) & 1 GHz \\ \(SR_{hip}^{law}\) & 0 dB \\ \(SR_{hip}^{high}\) & 5 dB \\ Weight of drone, \(W\) & 23.84 Newton \\ Rotor number, \(N_{R}\) & 4 \\ Horizontal flying speed of drone, \(v_{h}\) & 10 m/s \\ Rotor tip speed, \(v_{tip}\) & 102 m/s \\ Fuselage area, \(A_{f}\) & 0.038 m\({}^{2}\) \\ Drag coefficient, \(\tilde{C}_{D}\) & 0.9 \\ Rotor disc area, \(A_{r}\) & 0.06 m\({}^{2}\) \\ Profile drag coefficient, \(\Delta\) & 0.002 \\ Rotor solidity & 0.05 \\ Hovering time slot \(\Delta t\) & 60 s \\ Weighting factor of reward function \(\xi\) & 0.1 \\ Total number of episodes & 800 \\ Episode length & 60 min \\ Number of parallel envs for training rollouts & 12 \\ Number of network layers & 2 \\ Dimension of hidden layers & 512 \\ Activation function & ReLU \\ Learning rate & \(3\times 10^{-6}\) \\ Critic learning rate & \(5\times 10^{-4}\) \\ Number of PPO epochs & 15 \\ PPO clip parameter & 0.2 \\ Number of batches & 512 \\ Entropy term coefficient & 0.01 \\ Discount factor & 0.99 \\ GAE \(\lambda\) parameter & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Numerical parameters. To evaluate the performance of our approach, we use the following metrics:

* Coverage Ratio: The total number of users covered by the UAVs at each time step. * Energy Efficiency: The proportion of energy consumed by all the UAVs over the simulation period. * Reward: The cumulative reward obtained, reflecting the balance between coverage and energy consumption. To the best of our knowledge, there is no benchmark for the related research thus far. We compare our method with three other different deployment methods proposed in [24]. We redesign the details of the drone configuration in these methods according to our specific scenarios. We believe that these methods are more likely to produce intuitive and convincing results compared to ours, both in terms of whether the drone is moving and in the specific styles of the drone's movements. The comparison results are presented in Section 5.5 below. In addition, it is worth mentioning that each comparison method is derived from an average of 100 running results. ### Super-Parameter and Convergence Analysis

We first analyze the impact of the number of hidden layers in the algorithm on training. Considering the complexity of our problem, we analyze the training performance with two fully connected layers and three fully connected layers, respectively. Additionally, the following are the other settings in the experiment: the number of drones is four, and a high signal to inference and noise ratio is selected. As depicted in Figure 4, when the number of hidden layers is three, the network can initially learn and capture more features and complex relationships and, thus, lead to faster reward growth and quicker convergence. However, as the number of training episodes increases, the network with two hidden layers, despite converging slower, achieves better training results in the long run. Therefore, in this study, we set the number of network layers at two for the subsequent experiments. Here, we study the convergence performance of the proposed algorithm.