An intelligent control approach for heavy haul trains using deep reinforcement learning

One of the main challenges for the control of the heavy haul train of China is the cyclic air braking strategy on the long steep downward slopes. To address this problem, this paper proposes an intelligent control approach using a deep reinforcement learning algorithm to achieve safe operation, low maintenance costs and high running efficiency. The train control problem is firstly described considering the characteristics of the heavy haul railways of China. Then the cyclic air braking strategy is defined as a Markov decision process (MDP) and the key elements in the reinforcement learning framework are designed. To reduce the overestimation of action values in the Deep-Q-Network (DQN) based method, the Double DQN (DDQN) algorithm is used to solve the train control problem in the paper. The simulation experiments are conducted based on the real-word data of Shuozhou-Huanghua Line and the effectiveness of the DDQN-based approach is illustrated by comparing the performances of the proposed approach with those of the DQN-based method.