## 5 Conclusions and Future Work  
In this paper, a deep reinforcement learning algorithm with a local attention mechanism is proposed to address the scheduling problem of a single agile optical satellite with different task scales. Two techniques are effectively adopted to improve the performance of the algorithm: (1) local attention mechanism and (2) adaptive learning rate strategy. The local attention mechanism narrows the range of candidate tasks and selects the next-scheduled task from a more promising range, significantly improving the quality of the generated solution. The adaptive learning rate strategy dynamically decreases or increases the learning rate according to the reward curve trend in the training process, enhancing the early exploration and later convergence abilities. Based on these techniques, the proposed \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Task Number** & **50** & **75** & **100** & **125** & **150** & **175** & **200** & **225** & **250** \\ \hline \multirow{3}{*}{MPR} & DRLLA-a & **1.0** & **0.984** & **0.953** & **0.893** & **0.822** & **0.746** & **0.687** & **0.636** & 0.586 \\  & DRLLA-f & 0.924 & 0.890 & 0.852 & 0.810 & 0.766 & 0.720 & 0.673 & 0.632 & **0.593** \\  & DRLLA-e & 0.472 & 0.371 & 0.323 & 0.297 & 0.270 & 0.256 & 0.236 & 0.225 & 0.217 \\ \hline \multirow{3}{*}{STD} & DRLLA-a & **0** & **0.009** & **0.014** & 0.019 & 0.021 & 0.019 & 0.020 & **0.017** & **0.014** \\  & DRLLA-f & 0.045 & 0.048 & 0.045 & 0.042 & 0.035 & 0.035 & 0.033 & 0.028 & 0.030 \\  & DRLLA-e & 0.017 & 0.018 & 0.016 & **0.018** & **0.014** & **0.011** & **0.014** & 0.018 & 0.015 \\ \hline \multirow{3}{*}{MCT(s)} & DRLLA-a & 0.147 & 0.148 & 0.273 & 0.309 & 0.346 & 0.481 & 0.512 & 0.574 & 0.615 \\  & DRLLA-f & 0.083 & 0.140 & 0.212 & 0.278 & 0.356 & 0.445 & 0.545 & 0.652 & 0.685 \\ \cline{1-1}  & DRLLA-e & **0.048** & **0.066** & **0.086** & **0.114** & **0.137** & **0.167** & **0.193** & **0.220** & **0.257** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Testing results of the neural networks trained by three training algorithms on _Testing_R1_. Figure 10: Training process of DRLLA-a, DRLLA-f, and DRLLA-e. (**a**) Profit ratio curves of DRLLA-a, DRLLA-f, and DRLLA-e in the training process. (**b**) Learning rate curves of DRLLA-a, DRLLA-f, and DRLLA-e in the training process. algorithm exhibits superior performance in solution quality, generalization, and efficiency in comparison with the state-of-the-art algorithms. The experimental results also validate the effectiveness of the local attention mechanism for generating high-quality solutions and the adaptive learning rate strategy for improving the training effect. This paper can provide an efficient and effective approach for agile optical satellite scheduling, while there are still some areas for improvement. Some practical factors are not fully considered, such as the influence of cloud cover, lighting conditions, and observation angles on the imaging results. For future work, we will give a comprehensive consideration of various practical constraints and establish a more realistic AOS scheduling model. In addition, we will further improve the proposed algorithm to make it more available for practical application, and the algorithm will be extended to more complex instances, such as multi-AOS scheduling, emergency scheduling of AOSs, and uncertainty scheduling of AOSs. Conceptualization, Z.L. and W.X. ; Data curation, X.Y. ; Formal analysis, Z.L. ; Funding acquisition, W.X. ; Investigation, Z.L. and C.H. ; Methodology, Z.L. and W.X. ; Project administration, W.X. ; Resources, C.H. and X.Y. ; Software, Z.L. and C.H. ; Supervision, W.X. ; Validation, Z.L. ; Visualization, Z.L. and C.H. ; Writing--original draft, Z.L. ; Writing--review and editing, Z.L. and W.X. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry's and National Commission's Scientific Research Project (Grant No. TJ2022B020200-003-020). Not applicable.