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Multi-Agent Deep Reinforcement Learning Based Consistency Control in Train Platoon Systems

The train platoon (TP) in the urban rail transit (URT) system has been regarded as a promising solution to improve the transportation capacity between metro stations. Moreover, TP has a great potential to effectively adapt to the high dynamic flow of passengers during major events or festivals. Unfortunately, the promotion of TP is challenged due to the auto-block system in the traditional URT system as well as the complicated and dynamic operation environment. This paper investigates a cooperative control method to realize the consistency of TP in the premise of anti-interference. The multi-agent system of TP based on leader-follower typology is designed to achieve the operation goals in terms of distance and velocity. A novel distributed multi-agent deep reinforcement learning algorithm is proposed to obtain the optimal control strategy, where the artificial potential field is structured to provide prior knowledge of the environment. Compared with the conventional Q-learning based methods, simulation results validate that the proposed algorithm not only benefits the achievement of the operation goal, but also prevents the unfavorable interference from the external system.