Energy-efficient train control method based on soft actor-critic algorithm

Great attention has been attracted to the growing energy consumption of the subway system. It is significant to investigate the energy-efficient train control methodology which is regarded as an effective way to cut down the energy cost of the subway system. In this paper, a soft actor-critic-based method is proposed to determine the optimal driving strategy. Firstly, the inverse problem of the energy-efficient train control problem, i.e., minimizing the trip time with constant traction energy, is presented, and it is converted to a finite Markov decision process which can be solved with deep reinforcement learning (DRL) algorithms. Secondly, the soft actor-critic (SAC) algorithm is proposed to determine the optimal driving strategy. Finally, the effectiveness of the proposed method is verified via the case studies. It is also illustrated that the SAC-based method has a good performance in robustness and stability. Moreover, compared with other DRL algorithms such as the deep Q network (DQN), the SAC-based method saves about 66% training steps on average to achieve the optimum, which reduces the computational resources.