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.

翻译：作为煤矿最重要的辅助运输设备，矿用电机车目前仍多采用人工操作方式。然而由于煤矿环境复杂多变，近年来电机车安全事故频发。亟需一种能够适应不同复杂矿井环境的矿用电机车控制方法。强化学习关注智能体如何在环境中采取行动以最大化奖励，可助力实现矿用电机车的自动控制。本文阐述了如何将强化学习应用于矿用电机车的自主控制。为实现更精准的控制，我们进一步提出了一种改进的epsilon-greedy算法，该算法能更好地平衡探索与利用。为验证该方法的有效性，搭建了矿用电机车自主控制的联合仿真平台，可完成车辆的闭环仿真。仿真结果表明，在复杂不确定的煤矿环境中，该方法能确保电机车安全跟随前车，并在道路突发障碍时快速响应。