An improved ε-greedy strategy based reinforcement learning for autonomous control of mining electric locomotives

As a major auxiliary transportation equipment in coal mines, manually operated mining electric locomotives often cause accidents due to the complex and harsh mining environment. Reinforcement learning (RL) focuses on how agents take action in the environment to maximize returns, which is helpful for achieving automatic control of mining electric locomotives. In this paper, RL is applied to autonomous control of mining electric locomotives, considering unsafe conditions such as avoidance of dynamic obstacles and maintaining a safe distance from the vehicle in front. To achieve more precise control, an improved <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mi>ε</mml:mi> </mml:mrow> </mml:math> -greedy (IEG) algorithm which can better balance the exploration and exploitation is further proposed. 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.