Virtual Coupling (VC) is an emerging topic in the railway industry. It breaks the long-distance limitation and enables trains to operate at closer distances with new safety distance constraints. The new safety distance has high-order nonlinear time-varying because it is closer to the characteristics of virtually coupled trains (VCTs). This makes it difficult for the Automatic Train Operation (ATO) system to track the front virtually coupled train (VCT) in real-time and cannot guarantee stop synchronization. VCTs are equivalent to a physically connected train. So synchronous operation is very important. In order to solve the above problems, this paper takes the entire VCTs as optimization object and takes synchronized stopping, punctuality, and precise stopping as optimization indicators. The reinforcement learning Deep Q Network (DQN) algorithm is used to solve the optimization problem to seek the reference speed curve of each VCT. ATO performs real-time control on the basis of reference speed curve, which reduces calculation pressure and ensures synchronous operation of VCTs. Moreover, this paper improves DQN algorithm according to operation scenarios of VCTs to make the solution faster. Finally, we conduct a simulation experiment with the scene of Beijing Metro Line 11. The experiment demonstrates the effectiveness of this method for stopping synchronization and the superiority of the improved DQN algorithm for VCTs running scenarios.