# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 4 Experiments  
### Experimental Setting for AEOSSP  
#### 4.1.1 Datasets  
The instance generation of the AEOSSP follows the characteristics of the satellite resources and orbits. The parameters of the instances are randomly generated according to a normal distribution, which can better increase the conflict between tasks. The parameter distributions of the instances are shown in Table 2, and the satellite capability parameters are shown in Table 3.  
Based on the above rules, the experiment generates instances with tasks scaled 40, 60, 80, and 100. The instance with a task scale of 40 is shown in Figure 7, where the label means the task ID and profit. The figure shows that the task VTW distribution is relatively dense, and the conflicts of tasks are sufficiently large to effectively reflect the performance of the algorithms.  
We propose three indicators to measure the performance of the algorithms: the average scheduling profit (ASP), the average scheduling time (AST), and the percentage of excess  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Distribution** & **Details** \\ \hline \(\theta\)side & \(\text{U}(-\theta_{\text{max}},\theta_{\text{max}})\) & \(\theta_{\text{max}}=45^{\circ}\) \\ \(\chi\)1 & \(\text{U}(\lambda n_{tsk}+\text{t}\upsilon_{\text{max}}/2\), \(T_{plan}-\lambda n_{tsk}-\text{t}\upsilon_{\text{max}}/2)\) & \(\text{t}\upsilon_{\text{max}}=300\) s, \(T_{plan}=5400\) s \\ \(\text{t}\)side & \(\text{U}(\chi-\lambda n_{tsk},\chi+\lambda n_{tsk})\) & \(\lambda=12\) \\ \(c\)t & \(\text{U}(c_{\text{min}},c_{\text{max}})\) & \(c_{\text{min}}=5\) s, \(c_{\text{max}}=20\) s \\ \(pri\) & \(\text{U}(1,pri_{\text{max}})\) & \(pri_{\text{max}}=10\) \\ \(l_{\text{z}w}\)2 & \(\text{U}(\text{t}\upsilon_{\text{max}}/4,t_{\text{max}}/2)\) & \(t_{\text{max}}=300\) s \\ \hline \hline \end{tabular}  
* The intermediate time of the schedule period. 2 The length of the VTW. The above parameters are generated as integers.  
\end{table}
Table 2: Parameter distribution of the instances.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(\theta_{\text{max}}\) & Maximum roll angle & \(45^{\circ}\) \\ \(\varphi_{\text{max}}\) & Maximum pitch angle & \(45^{\circ}\) \\ \(\psi_{\text{max}}\) & Maximum yaw angle & \(90^{\circ}\) \\ \(Egy\) & Initial power & \(5000\) unit \\ \(\zeta\) & Minimum power & \(0.05\) unit \\ \(\theta_{0}\) & Initial roll angle & \(0^{\circ}\) \\ \(\varphi_{0}\) & Initial pitch angle & \(0^{\circ}\) \\ \(\psi_{0}\) & Initial yaw angle & \(0^{\circ}\) \\ \(t_{0}\) & Initial state time & \(0\) s \\ \(ute\) & Power consumption for attitude transition & \(2\) unit/s \\ \(uie\) & Power consumption for observation & \(2\) unit/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Satellite capability parameters.  
Figure 7: Instance with a task scale of 40.  
scheduling profit (PSP). The ASP measures the solution quality of the algorithm at different scales, the AST is used to reflect the timeliness of the algorithm, and the PSP indicates the difference in the ASP between the proposed method and other algorithms. All network training and experiments use NVIDIA TITAN RTX, i9-11900K CPU, and 64.0 GB memory. The algorithms are coded in Python, and the deep learning framework uses PyTorch 1.9.0.