## 1 Introduction  
Mining electric locomotives have the transportation function of materials, equipment, and people in roadway. Safe driving of mining electric locomotives is crucial. However, the method of underground mining is often used in the Chinese coal mining industry [1]. In deep underground confined spaces, there are unfavorable conditions for driving in coal mine roadway, such as slippery, muddy, dusty, foggy, and complex human behavior, which lead to frequent accidents. Reducing personnel participation and ensuring the safe operation of production and transportation equipment are necessary to ensure the safety production of coal mines. At present, the intelligent development of coal mine equipment has become an inevitable trend [2]. Unmanned transportation of coal mine equipment can fundamentally solve the problem of personnel participation and reduce casualties in the event of inevitable accidents. We have conducted relevant research on autonomous mining electric locomotives [3]. The mining electric locomotive has achieved functions based on Reinforcement Learning (RL) and improved \(\varepsilon\)-greedy such as autonomous and efficient operation on speed limited sections, maintaining a safe distance from vehicle in front, and avoiding obstacles. However, problems such as slipping of autonomous vehicles and wheel idling caused by slippery roadway in deep mines have not been addressed in a targeted manner. Manually driven mining electric locomotives rely on the driver's experience to sprinkle sand to increase wheel rail friction. But this method is a remedial measure taken when slipping/idling occurs during driving. And there is no quantitative evaluation standard for actions judged by human subjectivity. Moreover, actions based on human subjective judgment cannot be used as a quantifiable evaluation criterion applicable to machine autonomous decision-making. In the autonomous driving control process of mining electric locomotives, it is necessary to make decisions that can actively control the interaction between wheels and rails to prevent slipping, idling, and other phenomena caused by external environment. For rail vehicles, the destruction of the adhesion between the wheel and rail is the basic reason of wheel slip/idling [4]. One of the reasons for the traction and braking forces of wheelsets is the presence of contact and friction between the wheel and rail [5]. This kind of wheel rail interaction force is not pure friction, but a phenomenon called creep caused by deformation after wheel rail contact [6]. Reasonably utilizing the creep rate can improve the efficiency and safety of vehicle operation [7]. Nowadays, to prevent train wheel slipping or idling, scholars have conducted research on algorithms related to adhesion control. According to different control methods, adhesion control algorithms are generally divided into re-adhesion control and optimized adhesion control [8]. Re-adhesion control is a method that can quickly adjust the motor torque to achieve balance with the current adhesion conditions, and avoid wheel idling when determining whether the locomotive's wheels are idling/slipping. The research on this method is relatively mature [9; 10; 11], but it belongs to passive adhesion control method, with a low adhesion utilization rate and long algorithm response time. The purpose of optimizing adhesion control is to achieve the peak point of the optimal adhesion utilization, which is of great significance for the operation control of trains under wet, muddy and emergency braking conditions. [PERSON] et al. [12] analyzed the input and output phase shift dynamic characteristics of the traction system. They also proposed an adaptive control method that effectively controls the adhesion utilization rate of the traction system. [PERSON] et al.