## 4 Results
### Design of Experiments  
The scheduling horizon was from 2022/09/01 00:00 to 2022/09/01 24:00. As shown in Table 2, all scenarios were tested using two, three, and four AEOSs. The number of tasks changed from 300 to 1200 in increments of 300. Tasks were defined by the area corresponding to 114\(-\)124\({}^{\circ}\)E and 30\(-\)45\({}^{\circ}\)N and were distributed mainly in China. One training scenario and five test scenarios were used for each scale of task or satellite. In the following sections, X-Y represents scenarios in which the number of AEOSs is X and the number of tasks is Y. The storage consumption and observation duration for each task were randomly generated between [1.6 GB and 3.3 GB] and [15 s and 30 s], respectively. To standardize the description of profit, the tasks were discretized into nine levels. Here, we randomly assigned an integer in a range of 1-9 to each task as the profit value. The VTWs of the AEOSs for tasks were obtained from the System Tool Kit (STK). AEOS coverage analysis was performed using the STK, with the GAOFEN constellation simulated to compute acc \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Number of** & **Number of** & **Training** \\  & **AEOSs** & **Tasks** & **Scenarios** & \\ \hline
114–124\({}^{\circ}\)E, 30–45\({}^{\circ}\)N & 2,3,4 & 300, 600, 900, 1200 & 12 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation scenarios. target tasks. The angular velocities of the satellite pitch maneuver \(v_{p}\) and roll maneuver, \(v_{r}\), were both set to \(5^{\circ}\) /s, and the orbit parameters of the AEOSs are listed in Table 3.  
Hyperparameter tuning in DRL algorithms lacks fixed rules. We referenced approximate parameter ranges from the existing literature [59; 60] and finalized the parameters through pre-experiments. Table 4 lists the parameters of the proposed encoder-decoder network using in the training process. The parameter values were determined based on a pre-experiment implemented on scenarios with 2 AEOSs and 600 tasks (denoted as 2-600), in which the network with different values for each parameter was trained. The testing results are shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Maximum number of episodes & 50 \\ Memory capacity & 500 \\ Learning size & 200 \\ Batch size & 64 \\ Discount factor & 0.9 \\ Learning rate of actor & \(1\times 10^{-4}\) \\ Learning rate of critic & \(1\times 10^{-3}\) \\ Regularization parameter of entropy & \(1\times 10^{-2}\) \\ Soft update factor & 0.005 \\ Hidden dimension & 256 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training parameters. Figure 5: Results of the pre-experiment. Red star indicates the result of the setting values of the parameters in Table 4. The values of the parameters used for comparison are as follows: memory capacity = 250 (GB), 750 (GB); learning size = 100, 300; batch size = 32, 128; discount factor = 0.95, 0.99; learning rate of actor = \(1\times 10^{-3}\), \(1\times 10^{-5}\); learning rate of critic = \(1\times 10^{-2}\), \(1\times 10^{-4}\); regularization parameter of entropy = \(1\times 10^{-1}\), \(1\times 10^{-3}\); soft update factor = \(1\times 10^{-1}\), \(1\times 10^{-3}\); hidden dimension = 128, 512. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Semi-Major Axis** & **Orbital Inclination** & **Right Ascension of the** & **Orbital** & **Argument of** & **Mean Anomaly** \\ **(km)** & **(\({}^{\circ}\))** & **Ascending Node (\({}^{\circ}\))** & **Eccentricity** & **Perigee (\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline
7005 & 97.81 & 196.3165 & 0.0006736 & 208.4552 & 96.52 \\
7129 & 98.41 & 129.9734 & 0.0001996 & 89.5171 & 188.83 \\
7078 & 98.23 & 59.8261 & 0.0002885 & 111.2301 & 236.73 \\
7019 & 97.96 & 202.6863 & 0.0010966 & 93.4251 & 279.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Orbit parameters. The median of the running time results is denoted as t*, and the median of the total profit results is denoted as TP*.