Different from traditional operational research optimization algorithms, Deep Learning can solve combinatorial optimization problems in real time and has been widely used. However, these models based on pointer network have difficulty in obtaining features on the graph, they are not conducive to solving problems that are modeled on the graph. Secondly, as the structure of deep learning models becomes more complex, the explanation and analysis of the models becomes more difficult. There is a lack of interpretable work on models, which seriously hinders the development of Deep Learning. In order to solve these problems, a policy network that can effectively encode features on the graph and is interpretable is proposed. Specifically, a model structure in the field of graph neural network is introduced to extract the features on the graph, and a policy network is built, the network is trained using Reinforcement Learning; an agent-based interpretability method is used to mine the features that be used as explanation in the initial feature, these mined features are used to explain the actions of policy network.The effectiveness of the above methods is verified by experiments for solving the Traveling Salesman Problem: Policy network can effectively encode the features on the graph and has good generalization ability; The interpretability experiment shows that the actions of the policy network can be explained, which proves the interpretability of the policy network.

不同于传统的运筹学优化算法，深度学习可以实时解决组合优化问题，并得到了广泛的应用。然而，这些基于指针网络的模型难以获得图上的特征，不利于解决在图上建模的问题。其次，随着深度学习模型的结构变得越来越复杂，模型的解释和分析变得更加困难。模型上缺乏可解释的工作，严重阻碍了深度学习的发展。为了解决这些问题，提出了一种可以在图上有效编码特征并且可解释的策略网络。具体来说，引入了图神经网络领域的模型结构，用于提取图上的特征，并建立一个策略网络，使用强化学习对网络进行训练；一种基于代理的可解释性方法用于挖掘初始特征中用作解释的特征，这些挖掘的特征用于解释策略网络的行为。通过解决旅行商问题的实验验证了上述方法的有效性问题：策略网络可以有效地对图上的特征进行编码，具有很好的泛化能力；可解释性实验表明，策略网络的行为是可以解释的，证明了策略网络的可解释性。这些挖掘出来的特征被用来解释策略网络的行为。上述方法的有效性通过解决旅行商问题的实验得到验证：策略网络可以有效地对图上的特征进行编码，具有良好的泛化能力；可解释性实验表明，策略网络的行为是可以解释的，证明了策略网络的可解释性。这些挖掘出来的特征被用来解释策略网络的行为。上述方法的有效性通过解决旅行商问题的实验得到验证：策略网络可以有效地对图上的特征进行编码，具有良好的泛化能力；可解释性实验表明，策略网络的行为是可以解释的，证明了策略网络的可解释性。