单航天器对多目标的遍历交会任务规划是一类复杂度极高的混合整数优化问题，涉及顶层交会序列组合优化和底层飞行轨迹连续优化。现有方法将离散变量和连续变量一体优化，计算效率低且难以求得最优序列。提出了一种基于指针网络的多目标遍历交会序列规划方法，可快速获得最优序列。首先，构建了多目标遍历交会序列规划的神经网络模型，作为序列规划的决策智能体。其次，提出了一种基于异步优势函数行动者-评论家算法的无监督学习方法，避免了求解训练标签数据的计算开销。最后，为提高奖励函数的计算效率，在训练中嵌入了一种快速估计实际转移成本的近似方法。应用算例分析表明：所提出的训练方法可显著提高训练效率，经训练的决策智能体能够以超过88.7%的正确率快速求得最优序列。

Traversal rendezvous mission planning of multiple space targets for a single spacecraft is a mixed-integer programming problem with high complexity， which involves the combinatorial optimization of the top-level rendezvous sequence and the continuous optimization of the base-level flight trajectories. Existing methods that integrally optimize all discrete and continuous variables are inefficient and difficult to achieve the optimum. We propose a learning-based method that can efficiently obtain the near-optimal sequence mainly using the pointer networks. First， the neural network model for multiple-space-target traversal rendezvous planning is constructed as the decision agent of sequencing. Second， an unsupervised learning method based on the asynchronous advantage actor-critic algorithm is proposed to avoid the expensive computational cost in obtaining training labels. Finally， an estimation method to rapidly approximate the actual transfer cost is embedded in the training process to improve the efficiency of calculating rewards. Case studies show that the proposed training method performs efficiently， and the well-trained agent can rapidly predict the optimal sequence with a probability more than 88.7%.