## 4 Simulation Verification and Results
### Simulation Results  
The reward values for creep control of the mining electric locomotive obtained through simulation using traditional \(\varepsilon\)-greedy and improved \(\varepsilon\)-greedy algorithms are shown in the Figure 9. It can be seen that under the condition of training 500 times, the agent obtains larger reward values in the first half of the training using traditional \(\varepsilon\)-greedy. The increasingly stable creep control reward value is obtained in the later stages of training during the use of improved \(\varepsilon\)-greedy algorithm for autonomous driving simulation in mining electric locomotives. This indicates that the improved \(\varepsilon\)-greedy algorithm we have designed can fully utilize the experience gained from exploration in the first half of the training, while the exploration of traditional \(\varepsilon\)-greedy in the first half of the training is clearly limited. And towards the end of the training stage, the reward value tends to stabilize to ensure the reliability of the training results, allowing the agent to repeatedly train and verify the optimal value.  
We take the results every 125 iterations and plot the real-time position, speed, and creep rate of each wheel of the mining electric locomotive. From the 125 th iteration to the 500 th iteration (Figures 10 and 11), the mining electric locomotive can achieve their destination. However, the speed change is different. It can be easily observed that the mining electric locomotive cannot fully achieve the maximum speed limit for straight or curved roads during the initial training stage, and the vehicle cannot stop at its destination. As the training progresses, the mining electric locomotive acceleration increases.It can operate efficiently on speed limited sections, with accurate braking at the destination. On the first straight section of the road, the mining electric locomotive can maintain a safe distance when encountering obstacles.  
Figure 9: Results of reward with different values of \(\varepsilon\).  
Figure 11: The operating status of mining electric locomotives with episode = 375 and episode = 500.  
Figure 10: The operating status of mining electric locomotives with episode = 125 and episode = 250.  
of some wheels slipping at certain times during the turning process. After training the agent, it can be seen that as the number of iterations increases, the idle time during the vehicle acceleration process significantly decreases, almost eliminating the phenomenon of the vehicle slipping on curves. When the intelligent agent is trained 500 times, the creep rate of the vehicle during acceleration can be controlled within the range of 0.1 to 0.4. This indicates that the creep rate between the wheels and rails is well controlled during vehicle operation and is well utilized to drive wheel acceleration. This proves that the design of our learning algorithm is reasonable and effective, which can better utilize the creep rate and achieve the goal of safe and efficient creep control.  
We also conduct simulation experiments on the driving results of an 8-ton electric locomotive in an 86 m tunnel with a friction coefficient of 0.2. The results are detailed in Appendix C. From the results in Appendix C, it can be concluded that this method can be applied to different mining electric locomotives traveling on different track conditions. This method has good universal applicability in the control of creep rate and obstacle avoidance of mining electric locomotives.