###### Abstract
Furthermore, this model interacts with the outputs of the encoder and decoder to get the matching degree of tasks, which is beneficial for selecting the suitable task. According to these studies, a neural model based on DRL-HA can be utilized to solve TSP, VRP, backpack, and other NP-hard problems. Therefore, based on these studies, this paper introduces the DRL-HA model to the AEOSSP problems and investigate its validity. Because the AEOSSP is a NP-hard problem, for the traditional heuristic algorithms, the search difficulty and time to obtain the solution increase sharply with the increase of the problem scale. Thus, the traditional scheduling algorithms are not able to satisfy the requirements regarding the high-profit and high-timeliness of agile satellite scheduling, such as the ability to create rapid adjustments for emergency tasks. Meanwhile, in large-scale scheduling scenarios, the solution time of the traditional intelligent optimization algorithm is unacceptably lengthy. Additionally, in a specific scenario, the demand of user observation has a certain regularity, while the existing algorithms do not take this phenomenon into the consideration. With the continuous application of the DRL-HA in combinational optimization problems, the advantages of high efficiency and effective utilization of historical data are gradually discovered. Therefore, the research on agile satellite scheduling based on DRL-HA is consequential to improve the emergency response ability, rapid response ability and efficiency of satellite-earth observation system, which potentially possess values in theory and application. With this innovative algorithm, when agile satellites implement tasks such as environmental surveillance, disaster relief, and intelligence reconnaissance, the high-profit and high-timeliness of agile satellites tasks can be guaranteed. From the analysis above, the traditional algorithms are not able to render time and quality to be satisfied simultaneously. When these traditional algorithms are applied, large-scale task scheduling scenarios usually require an unacceptable amount of time. Furthermore, neither the characteristic information of the task set nor historical scheduling scenarios are comprehensively taken into consideration. When switching to a new scheduling scenario, the algorithms search and iterate again, which wastes a large amount of time. Therefore, to rectify these disadvantages, the following problems should be solved:  
1. How to comprehensively deploy dynamic task information and scheduling scenario information;
2. How to establish the DRL-HA model aiming to solve the AEOSSP;
3. How to utilize convoluted constraints for establishing an efficient model. This study utilizes the DRL-HA model to comprehensively deploy the task characteristic information and historical scheduling information for solving AEOSSP with a high-quality solution and relatively less time. The remainder of this paper is organized as follows. In Section 2, we put forward a mathematical description of the AEOSSP. In Section 3, a heuristic algorithm based on DRL-HA for AEOSSP is introduced in detail. We set up experiments to verify the effectiveness of the proposed algorithm in Section 4, and the experimental results are displayed in Section 5. The conclusions are drawn in Section 6.