East China Jiaotong University

Chang Tan

Hui Yang

Zhongqi Li

Yating Fu

Yu Wang

The present disclosure provides a method and system for controlling a heavy-haul train based on reinforcement learning. The method includes: obtaining operation state information of a heavy-haul train at a current time point; obtaining a heavy-haul train action of a next time point according to the operation state information of the heavy-haul train at the current time point and a heavy-haul train virtual controller, and sending the heavy-haul train action of the next time point to a heavy-haul train control unit to control operation of the heavy-haul train. The heavy-haul train virtual controller is obtained by training a reinforcement learning network according to operation state data of the heavy-haul train and an expert strategy network; the reinforcement learning network includes one actor network and two critic networks; the reinforcement learning network is constructed according to a soft actor-critic (SAC) reinforcement learning algorithm.

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Nanchang, China

Chang Tan