## 1 Introduction
[11] considered the time-dependent profits and presented an adaptive-directional dynamic programming algorithm with decremental state space relaxation for the single-orbit scheduling of the single agile satellite. The above studies summarily prove that exact algorithms can explore the whole search space and obtain the optimal solution, and they are applicable to the single-orbit or small-scale satellite scheduling. However, as the problem scale expands, the computational cost of the exact algorithms tends to be unacceptable because of the NP-hard characteristic and the complex constraints. Differing from exact algorithms, heuristic algorithms can iteratively search for a good solution with relatively lower computational cost, and they have been extensively applied to the agile satellite scheduling due to the excellent abilities of exploration and exploitation, such as genetic algorithms (GAs) [12; 13], particle swarm optimization (PSO) algo  
Figure 1: Observation process of the traditional optical satellite and AOS. rithms [14; 15], ant colony optimization (ACO) algorithms [16; 17], artificial bee colony (ABC) algorithms [18] and so on. [PERSON] and [PERSON] [19] formulated a mixed-integer non-linear optimization model and proposed an elitist mixed coded genetic algorithm algorithm to solve the agile satellite scheduling problem. [PERSON] et al. [20] considered the drift angle constraint of the observation instrument and developed an improved ant colony algorithm based on a sensational and consciousness strategy to solve the area target observation problem of agile satellites. [PERSON] et al. [21] improved the three search phases of the basic ABC algorithm and presented a hybrid discrete artificial bee colony (HDABC) algorithm to address the satellite scheduling problem. These heuristic algorithms can optimize solutions through superior search mechanisms and iterative updates of population, but the issue of considerable computational time and the convergence problem still exist. To avoid the above shortcomings, researchers demand for a novel, efficient, and non-iterative method to solve the agile satellite scheduling problem. With the development of artificial intelligence technology, deep reinforcement learning (DRL) methods have achieved excellent results in many fields [22; 23; 24; 25], especially in large-scale combination optimization problems [26; 27]. Because the agile satellite scheduling problem can be formulated as a sequential decision problem [28; 29], DRL algorithms can be adopted to replace heuristic algorithms, which can directly generate solutions without an iterative search. [PERSON] et al. [30] developed an end-to-end network model with a convolution neural network (CNN) as the encoder, a gated recurrent unit (GRU) as the decoder, and an attention mechanism to obtain the relevance between the output task and each input task, and this model achieved good results in the small-scale scheduling instances. [PERSON] et al. [31] proposed a two-phase neural combination optimization method, which adopted a Pointer Network (PtrNet) to generate a permutation of executable tasks and a deep deterministic policy gradient algorithm to determine the observation time of selected tasks. [PERSON] et al. [32] proposed a DRL and parameter transfer-based approach (RLPT), consisting of a task encoder composed of a GRU, a feature encoder composed of a CNN, and a decoder composed of an attention mechanism. These studies have preliminarily verified the feasibility and effectiveness of DRL, but there are still some weaknesses. First, the neural networks in these methods can only deal with the inputs of fixed dimensions, and no effective approach is provided to handle the inputs of variable dimensions, especially VTWs, the number of which may be different for different tasks. Second, the above methods based on the encoder-decoder structure all adopt the global attention mechanism whose computation is a bit large, and they do not pay attention to the internal connections among tasks.