安庆师范大学学报（自然科学版）2024，Vol.30Issue(2)：62-68,7.DOI:10.13757/j.cnki.cn34-1328/n.2024.02.011
基于改进的指针网络深度强化学习算法求解旅行商问题
Improved Deep Reinforcement Learning Algorithm Based on Pointer Network for Traveling Salesman Problem
摘要
Abstract
Traveling salesman problem is a classic problem in combinatorial optimization. The development of deep rein-forcement learning provides a new way to solve this problem. In the deep reinforcement learning algorithm based on the point-er network for the traveling salesman problem, the encoders of the strategy network and the value network both employ the complex long short-term memory network structure, which leads a long training time to the large-scale traveling salesman problem. Considering the independence of the position order among the input nodes, this paper modifies the recurrent neural network of the encoder in the pointer network and replaces the long short-term memory network of encoders in the strategy network and the value network with the one-dimensional convolutional neural network. An improved deep reinforcement learning algorithm based on the pointer network is proposed, which reduces the training time by 12％to 15％compared with the original model on the same scale of resolving the problem. The experimental results verify the effectiveness of the im-proved algorithm.关键词
旅行商问题/深度强化学习/指针网络/卷积神经网络/长短期记忆网络/策略梯度Key words
traveling salesman problem/deep reinforcement learning/pointer network/convolutional neural network/long short-term memory/policy gradient分类
信息技术与安全科学引用本文复制引用
唐娇娇,左烔菲,陈逢林..基于改进的指针网络深度强化学习算法求解旅行商问题[J].安庆师范大学学报（自然科学版）,2024,30(2):62-68,7.基金项目
安徽省教育厅重点项目(KJ2019A0580)和安徽省教育厅教研项目(2020xsxxkc259) （KJ2019A0580）
