我所李润梅老师课题组完成的论文《An Actor-Critic Framework Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling Problem》于2024-12在《IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI)》正式发表。论文摘要如下：
Abstract: High-speed trains are inevitably affected by emergencies in their daily operation, which may cause the trains to fail to run according to the original planned timetable. Therefore, how to adjust the timetable of subsequent trains in emergencies is crucial for railway operations. This paper introduces a deep reinforcement learning algorithm based on an actor-critic network, which minimizes the total delay time by defining the departure order as an action. Numerical experiments are performed on the timetable of Beijing-Shanghai high-speed railway line.
引用信息：Zhilong Tian, Runmei Li, Gang Xiong, Fenghua Zhu. An Actor-Critic Framework Deep Reinforcement Learning Approach to High-speed Train Timetable Rescheduling Problem. IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI), 2024-12. DOI: 10.1109/dtpi61353.2024.10778851