物联网学报2025，Vol.9Issue(3)：37-47,11.DOI:10.11959/j.issn.2096-3750.2025.00462
车联网中基于迁移强化学习的跨域充电站推荐算法
A transfer reinforcement learning-based approach for cross-domain charging station recommendation in the Internet of vehicles
摘要
Abstract
Deep reinforcement learning has been widely applied in charging station recommendations in the internet of ve-hicles,but training separate neural networks for each region are often required by traditional methods,leading to in-creased computational load and data demands.Transfer learning accelerates the learning process for new tasks by leverag-ing knowledge from previous tasks,thus reducing redundant training.Therefore,a transfer reinforcement learning-based cross-domain charging station recommendation algorithm was proposed.An embedding encoder was introduced by this algorithm to align the system state and action space dimensions between the source and target domains,effectively solv-ing the dimensionality discrepancy problem.Additionally,variational distributions were constructed based on mutual in-formation to maximize the similarity between pre-aligned and post-aligned target domain states to ensure effective trans-fer.Compared to three typical charging station recommendation algorithms,in the low-dimensional to high-dimensional transfer,the average total charging time of the proposed algorithm was reduced by 57.6％,59.3％,and 7.1％.In the high-dimensional to low-dimensional transfer,the reductions were 12.3％,40.8％,and 4.7％,respectively.Simulation results demonstrate that the proposed algorithm exhibits strong transferability and significantly enhances the performance of cross-domain charging station recommendation systems.关键词
深度强化学习/迁移学习/互信息/充电站推荐Key words
deep reinforcement learning/transfer learning/mutual information/charging station recommendation分类
信息技术与安全科学引用本文复制引用
林海,赵家仪,曹越,苏航宇,王丽园..车联网中基于迁移强化学习的跨域充电站推荐算法[J].物联网学报,2025,9(3):37-47,11.基金项目
国家重点研发计划项目(No.2023YFB3907105) （No.2023YFB3907105）
湖北省重点研发计划项目(No.2023BAB022)The National Key Research and Development Program of China(No.2023YFB3907105),Hubei Province Key Research and Development Program(No.2023BAB022) （No.2023BAB022）
