###### Abstract  
The agile earth observation satellite scheduling problem (AECSSP), as a time-dependent and arduous combinatorial optimization problem, has been intensively studied in the past decades. Many studies have proposed non-iterative heuristic construction algorithms and iterative meta-heuristic algorithms to solve this problem. However, the heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative meta-heuristic algorithms accomplish a high-quality solution with a lot of time. To overcome the shortcomings of these approaches and efficiently utilize the historical scheduling information and task characteristics, this paper introduces a new neural network model based on the deep reinforcement learning and heuristic algorithm (DRL-HA) to the AEOSSP and proposes an innovative non-iterative heuristic algorithm. The DRL-HA is composed of a heuristic construction neural network (HCNN) model and a task arrangement algorithm (TAA), where the HCNN aims to generate the task planning sequence and the TAA generates the final feasible scheduling order of tasks. In this study, the DRL-HA is examined with other heuristic algorithms by a series of experiments. The results demonstrate that the DRL-HA outperforms competitors and HCNN possesses outstanding generalization ability for different scenario sizes and task distributions. Furthermore, HCNN, when used for generating initial solutions of meta-heuristic algorithms, can achieve improved profits and accelerate interactions. Therefore, the DRL-HA algorithm is verified to be an effective method for solving AEOSSP. In this way, the high-profit and high-timeliness of agile satellite scheduling can be guaranteed, and the solution of AEOSSP is further explored and improved. a 2022 20 September  
satellite requires some time, which we call transition time in this paper, to maneuver from a previous position to another one [1]. Any change in observation angles and positions between two consecutive tasks affects the starting time of the task, rendering this problem highly time-dependent. Thus, greater mobility creates more flexible observation angles and time-dependent features, magnifying the difficulty of the AEOSSP. Many studies have proposed non-iterative heuristic construction algorithms and iterative metaheuristic algorithms to solve the AEOSSP. When designing these two types of heuristic algorithms, scholars should comply with some principles, such as the earliest start principle [2], the least possible conflict principle [3], and the maximum imaging quality principle [4], etc. With these principles, [PERSON] et al. [5] studied the Pleiades agile satellite launched by the French Space Agency and comprehensively described the AEOSSP for the first time. Based on [PERSON]'s research, numerous related algorithms have been proposed. The heuristic construction algorithm contains a systematic structure and does not generate current status information in the solution process. With these algorithms, the accepted approximate solutions can be obtained within a short time. Many studies were made for developing the heuristic construction algorithms by establishing distinct models [6; 7; 8] with different theories and introducing indicators based on different task features [9]. However, because these algorithms' systematic structures do not update the historical scheduling information in the solution process, global information features are lost and the AEOSSP is not solved effectively. For addressing these defects, the iterative metaheuristic algorithms that evaluate the local solution and update the historical scheduling information are tendered. They are similar to genetic and simulated annealing algorithms with certain randomness and generality. Because genetic algorithms are highly adaptable when combined with other models and algorithms, most of them belong to the class of improved genetic algorithms [10; 11; 12; 13], raising the quality of the solutions.