Hierarchical Reinforcement Learning for Long‐Distance Multi‐Objective Optimization of Heavy‐Haul Trains via Sequential Task Assignment Across Spatial Segments

ABSTRACT

The optimization of long‐distance multi‐objective operations for heavy‐haul trains faces low solution efficiency when applying intelligent algorithms. Currently, few studies consider the changing emphasis of multiple operational objectives, such as safety, stability, punctuality and energy efficiency changes across different operational segments through task switching. To address this issue, this paper proposes a sequential multi‐task collaboration hierarchical reinforcement learning framework for dynamic multi‐objective optimization in long‐distance operations. First, an event‐driven time‐dependent classification model is established to decompose the long‐distance optimization problem into multiple subtasks, thereby reducing optimization complexity. Furthermore, a hierarchical reinforcement learning method based on sequential multi‐task collaboration is designed, in which segment‐specific subtask policies are trained in parallel and invoked sequentially along the route, while global coordination is achieved through a top‐level dynamic programming framework. Simulation results based on a real 340‐km railway line demonstrate that, compared to a single‐agent global optimization method, the proposed framework improves convergence speed by nearly 40% and achieves superior optimization quality. It effectively restricts coupler forces within the safe limit of 1100 kN, while successfully balancing multiple objectives including speed tracking and energy consumption.