# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Testing Performance  
#### 4.3.1 Model Generalization Test  
We transferred a trained model to other instances in the training dataset. Table 4 and Figure 6 demonstrate the generalization ability of a well-trained agent in different instances of training. This is mostly attributed to the similarities of the planning tasks and the significant generalization ability of the D3 RL model. Initially, a scenario was randomly sampled from the training set for each instance size to train the scheduling network. Afterwards, the trained network was utilized to process the remaining training instances in the set (the results are denoted as _trained_). This was primarily performed to assess the model's generalization ability. Models that perform well in unknown training scenarios demonstrate reduced reliance on specific data and require a smaller training set. The variable \(train\_num\) in Table 4 represents the average training  
Figure 5: Training curves for the Area and World scenarios. The two plots show the average scheduled rate of Area and World instances, respectively. The results were obtained by conducting experiments with 5 different random seeds. (**a**) Training process for Area targets. (**b**) Training process for World targets. Figure 6: Optimization scheduled rate performance contrast. Boxplot illustrating comparison based on 10 different instances. Each box represents the interquartile range (IQR) of the data distribution, with the median marked by a horizontal line inside the box. Triangle markers indicate the mean values of the test results. (**a**) Scene: Area. (**b**) Scene: World. results obtained from 10 unknown scenes in the training set. The training process consisted of a specific number of iterations denoted by the variable \(num\), which can take three values: 50, 500, and 5000. As shown in the training curves of Figure 5, the training process was considered to be completely convergent when iteration times exceeded 5000. In the _Area_ data (Figure 6a), the scheduled rate obtained through model inference (\(trained\)) can even reach 91.9% to 98.5% of model converged (\(train\_5000\)). Similarly, in the \(World\) instances (Figure 6b), the scheduled rate can range from 93.3% to 96%. In the \(World\) instances depicted in Figure 6b, the results of direct model inference (\(trained\)) exhibit a similar pattern to the _Area_ instances. However, the task scheduled rate does not simply decrease as the number of tasks increases. Interestingly, when the observed targets increase from 300 to 400, the task scheduled rate actually improves. This phenomenon can be attributed to the fact that the observed targets in the \(World\) instances are extremely sparse compared with the targets in the _Area_ instances. Increasing the number of observable target points can enhance the task scheduled rate in scenarios where there are relatively few target points to begin with. In the results depicted in Figure 6, for the \(train\_num\) results, it is evident that as the instance size increases, the height of the boxes consistently decreases. With 50 iterations of training, the model's training remains incomplete, resulting in a more dispersed distribution of outcomes. This corresponds to the training process. Within the _Area_ scenario results, the results of _trained_ also become more concentrated with the enlargement of the instance size. However, this characteristic is not observed within the \(World\) scenario. This discrepancy could potentially be attributed to the larger geographical scope and the significant variations in the distribution of target points in the \(World\) scenario. We have conducted some 'transfer' experiments (i.e., trained the model on the World, then evaluated it on some Area setups) and found that the model's generalization performance between \"Area\" and \"World\" scenarios is not satisfactory. The inference results of the well-trained model in \"Area\" were slightly lower than training a new model for the \"World\" scenario 500 times.