Leveraging Deep Reinforcement Learning for Reducing Longitudinal Train Forces in Railway Systems
摘要
This paper explores the application of deep reinforcement learning to minimize in-train forces. A longitudinal train dynamics model incorporating differential equations is established. Track profile identification enables realistic computational experiments. Deep reinforcement learning is used to explore control strategies for braking and traction forces, aiming to minimize in-train forces and improve railway safety and efficiency.