Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem
This study addresses the vehicle rescheduling problem under traffic disruptions in dense railway networks, where traditional operations research and existing reinforcement learning approaches face limitations in real-time responsiveness, scalability, and multi-agent coordination. To overcome these challenges, the authors propose a semi-hierarchical deep reinforcement learning architecture tailored to railway operational constraints, decoupling scheduling decisions from path planning. The framework employs hierarchical action and observation spaces to separately handle sparse high-level scheduling commands and frequent low-level path updates. Experiments on the Flatland-RL platform demonstrate that, across scenarios involving 7 to 80 trains, five difficulty levels, and 50 random seeds, the proposed method nearly doubles the number of trains successfully reaching their destinations, maintains deadlock rates below 5%, and adaptively executes scheduling actions such as reordering, delaying, or canceling trains, thereby significantly enhancing collaborative efficiency and robustness in complex congestion scenarios.