Predictive Mamba-Enhanced Multi-Agent Reinforcement Learning Control for Virtual Coupling of High-Speed Trains

Virtual coupling control of trains is a promising technology for improving railway capacity and operational efficiency. However, existing multi-agent reinforcement learning (MARL) approaches struggle to capture long-sequence temporal dependencies among train states in complex multi-train interaction scenarios, resulting in limited robustness and coordination stability. To address this issue, this paper proposes a Predictive Mamba-based Multi-Agent Soft Actor–Critic (PM-MASAC) framework. A Mamba-based state prediction module is embedded into the centralized Critic network to model historical state sequences and generate predictive state representations, thereby enhancing value estimation accuracy. In addition, a multi-agent aggregated prioritized experience replay (PER) mechanism is introduced to improve the utilization of critical cooperative samples and stabilize training. A hierarchical local–global reward structure is further designed to ensure individual tracking performance while promoting overall formation coordination. Experimental results under realistic railway operating conditions demonstrate that PM-MASAC achieves superior robustness compared with baseline MARL methods. Velocity and spacing tracking errors are maintained within 3% and 1%, respectively, and the steady-state formation success rate exceeds 95.7% in the training environment.