Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

To realize a better automatic train driving operation control strategy for urban rail trains, an automatic train driving method with improved DQN algorithm (classical deep reinforcement learning algorithm) is proposed as a research object. Firstly, the train control model is established by considering the train operation requirements. Secondly, the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem. Finally, the priority experience playback and “restricted speed arrival time” are used to reduce the useless experience utilization. The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions. From the experimental results, the train operation meets the ATO requirements, the energy consumption is 15.75% more energy-efficient than the actual operation, and the algorithm convergence speed is improved by about 37%. The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation, thereby contributing meaningfully to the advancement of automatic train operation intelligence.