###### Abstract
Furthermore, while some algorithms are designed for single-objective AEOSSPs, metaheuristic algorithms can also be utilized for multi-objective optimization [12; 14; 15]. With these iterative metaheuristic algorithms, the solutions are outstanding, but the surplus time and space complexity cause their inferior efficiency. In conclusion, there are some inevitable weaknesses in both two types of algorithms. The heuristic construction algorithms spend a relatively shorter time at the expense of solution quality, while the iterative metaheuristic algorithms accomplish a high-quality solution with a lot of time. The traditional heuristic algorithms usually calculate the priority of each task according to the task characteristics and then schedules them based on the acquired priority. The static characteristics of the task are usually adopted in the heuristic functions. In general, when the observation target is determined, the constant parameters obtained through simulation calculation are considered as static characteristics, such as the return of the task, the start time of the visual time window (VTW), the duration time of the task, etc. During the whole scheduling process, these characteristics remain a fixed value. The flow of this type of algorithm is shown in Figure 2. Firstly, the scheduling scenario is  
Figure 1: Diagram of agile satellite performing observation tasks. initialized. Then, the priority sequence of the tasks to be observed is obtained through the heuristic function. Generally, only static characteristics of the task are taken into consideration in the traditional heuristic algorithm. Since its solution process does not produce current state information, there is no historical scheduling information, which is significant to improve the effectiveness of the solution. In addition, most of the global information characteristics would be lost. Based on the existing research about the development of artificial intelligence, the new neural network models provide new ideas for solving AEOSSP problems. The AEOSSP aims to rationally allocate limited resources to observation tasks for satisfying the users' diversified demands and maximizing schedule profits, which is a classical combinational optimization problem. Many studies [16; 17; 18; 19; 20] were conducted to verify the effectiveness of neural network models for solving scheduling problems. Among them, the deep reinforcement learning utilized in the heuristic algorithm (DRL-HA) [21] was gradually developed by a series of research. [PERSON] et al. [22] proposed a printer network (PN), applied to the sequence-to-sequence (Seq2 Seq) structure with encoding and decoding as two recurrent neural networks (RNNs). PN applies an attention mechanism, which reaches a one-step decision by analyzing the outputs of encoding and decoding. [PERSON] et al. [23] developed the attention mechanism and used the strategy gradient method and actor-critic algorithm (AC) for training the network model. [PERSON] et al. [24] simplified the encoding process of the pointer network by abandoning the RNN structure and adopted different attention mechanisms for the decoder. Compared to the previous research, the efficiency is greatly improved. However, there is one inevitable weakness of this model: one vehicle's dynamic parameter remains an identical value for individual tasks, rendering the deviation of the solution. [PERSON] [21] solved this defect and proposed an innovative attention model (AM) by introducing Transformer, a natural language process. Unlike the previous research, which abandons RNN, this study took Transformer encoder's multihead-attention (MHA)  
Figure 2: Traditional heuristic algorithm flow diagram of static parameter input. as the model's encoder. In this way, the embedding layer is able to gain the individual task characteristic information and enter it into the MHA layer for obtaining global variables of the current task set.