## 5 Discussion  
With the development of space science and technology, the collaborative operation of multiple AEOSs has shown advantages in enhanced remote sensing services, including speed. An effective scheduling algorithm is the key to improving the efficiency of AEOS applications. This study proposed a DRL-based TPHO framework with an effective heuristic rule, MRC, to rapidly generate an observation plan for the MAEOSSP. During the training process, the total profit increases at the beginning and then oscillates around a specific value, demonstrating that the network can converge on a stable policy. After training, the proposed method can be generalized to various application scenarios. According to the testing results, the proposed TPHO-MRC algorithm significantly outperforms other algorithms. Comparison results also indicate that the TPHO-based algorithms show good time efficiency and have the potential to apply to larger-scale MAEOSSPs.  
To further discuss the performance of the proposed TPHO-MRC, boundary value analysis of algorithm applicability was conducted. In the experimental section of this study, tasks were divided into nine priority levels based on importance, i.e., the task profit range was [1, 9]. Task reward serves as crucial information for the proposed algorithm's task sequence decision-making. Here, we analyze the training convergence when differences in task importance decrease. Figure 10(a) shows the training curves when task profits are in ranges [1, 3], [1, 2], and all equal to 1. When there are only two profit levels, the algorithm can still converge near the maximum value of the training curve. When all profits are equal, although the algorithm converges, it fails to approach the maximum value, indicating that the current algorithm is more suitable for scenarios with distinct task priorities.  
In our experiments, task observation durations ranged from d* = [15 s, 30 s]. We analyze the training convergence when extending observation durations. Figure 10(b) displays training curves for 5\(\times\), 6\(\times\), 7\(\times\) and 8\(\times\) original durations. The algorithm converges to maximum values at 5\(\times\) and 6\(\times\) duration, converges near initial values at 7\(\times\), and completely fails at 8\(\times\) duration, demonstrating current applicability for observation durations within [90 s, 180 s].  
For storage requirements, tasks occupied s* = [1.6 GB, 3.3 GB] with satellite capacity of 350 GB. Figure 10(c) shows training curves when storage demands increase to 5\(\times\), 10\(\times\), 20\(\times\) and capacity reduces to 1/10. The algorithm maintains convergence to maximum values in both scenarios, indicating broad adaptability to storage requirement variations.  
MAEOS scheduling usually serves as an oversubscription scenario in which the task load exceeds the resource ability. The proposed TPHO-MRC has shown the ability to make good selections from excess tasks. We also compared TPHO-MRC and TPHO-Profit with TPHO-EFT in two different memory capacity scenarios. The comparison results  
Figure 11: The training curves when there are differences in task attributes. (**a**) The training curves when task profits are in ranges [1, 3], [1, 2], and all equal to 1; (**b**) the training curves for 5\(\times\), 6\(\times\), 7\(\times\) and 8\(\times\) original observation durations; (**c**) the training curves when storage demands increase to 5\(\times\), 10\(\times\), 20\(\times\) and capacity reduces to 1/10.  
demonstrate that the proposed MRC rule is an intuitive and effective resource allocation rule in capacity-limited application scenarios.