Autonomous Operations With a Safe Reinforcement Learning Approach for Urban Rail Transit

Reinforcement learning has increasingly showcased its potential in decision-making for the autonomous operation of urban rail transit. However, the inability of reinforcement learning to ensure safety during both the learning and execution phases presents a significant barrier to its practical application. This limitation makes it challenging to implement reinforcement learning in safety-critical domains. In urban rail transit, it is reflected in generating control command sequences that keep the train’s speed consistently below the speed limit. To address this issue, a framework is proposed for intelligent control of autonomous urban rail transit trains, referred to as SSA-DRL (Shield-Searching-Additional-DRL). This framework comprises four modules: a post-posed Shield, a Searching Tree, an Additional Learner, and a DRL framework. It effectively satisfies speed and schedule constraints while optimizing operational processes. The framework is evaluated across sixteen different sections, demonstrating its effectiveness through both basic simulations and additional experiments.