  As the most important auxiliary transportation equipment in coal mines,
mining electric locomotives are mostly operated manually at present. However,
due to the complex and ever-changing coal mine environment, electric locomotive
safety accidents occur frequently these years. A mining electric locomotive
control method that can adapt to different complex mining environments is
needed. Reinforcement Learning (RL) is concerned with how artificial agents
ought to take actions in an environment so as to maximize reward, which can
help achieve automatic control of mining electric locomotive. In this paper, we
present how to apply RL to the autonomous control of mining electric
locomotives. To achieve more precise control, we further propose an improved
epsilon-greedy (IEG) algorithm which can better balance the exploration and
exploitation. To verify the effectiveness of this method, a co-simulation
platform for autonomous control of mining electric locomotives is built which
can complete closed-loop simulation of the vehicles. The simulation results
show that this method ensures the locomotives following the front vehicle
safely and responding promptly in the event of sudden obstacles on the road
when the vehicle in complex and uncertain coal mine environments.
