Roadmap and challenges for reinforcement learning control in railway virtual coupling

Abstract The ever increasing demand in passenger and freight transportation is leading to the saturation of railway network capacity. Virtual Coupling (VC) has been proposed within the European Horizon 2020 Shift2Rail Joint Undertaking as a potential solution to address this problem. It allows to dynamically connect two or more trains in a single convoy, thus reducing headway between them. In this work, we investigate the main challenges related to the potential deployment of VC in railways. Its feasibility through Reinforcement Learning techniques is explored, discussing about technical implementation and performance issues. A qualitative analysis based on a Deep Deterministic Policy Gradient control algorithm is proposed. The aim is to give a first insight towards the definition of a qualitative and technology roadmap which could lead to the deployment of artificial intelligence applications aiming at enhancing rail safety and automation.