Optimizing Train Scheduling in Heavy-Haul Railways Using Diversified Cooperative Deep Reinforcement Learning

With the increasing role of railway transportation in the globalized economy, the issue of loading delays has emerged as a critical factor affecting the efficiency and reliability of rail transport. This paper addresses the loading-delay problem in heavy-haul railway transportation by proposing an optimization method based on a diversified cooperative deep reinforcement-learning network (DCD-RLN). Within an actor–critic framework, this method integrates optimization algorithms with loading-delay models to develop a tailored strategy for adjusting train schedules during delays. The proposed approach considers constraints such as empty and loaded unit trains, coupling requirements, and competitive and cooperative interactions among multiple intelligent agents, aiming to minimize assembly times for unit trains and reduce waiting and assembly times during loading and coupling phases for loaded unit trains. Comparative experimental results with existing solutions demonstrate the significant advantages of the DCD-RLN method in enhancing the robustness and efficiency of train operations. This study not only provides a domain-specific reinforcement-learning framework for railway-transportation researchers but also offers valuable references for practical operations in railway-transportation enterprises.