## 5 Results and Discussion
### Contributions of Knowledge-Assisted Learning Methods  
To verify the contributions of the knowledge-assisted learning methods in the learning process of KA-DDPG, we trained the KA-DDPG algorithm, with three cases. The configurations of these cases were as follows: (1) the KA-DDPG, including the criteria action and guiding reward methods (i.e., the algorithm proposed in this work); (2) the KA-DDPG, only including the guiding reward method; and (3) the KA-DDPG, only including the criteria action method. As the skid-steering vehicle studied in this paper is an overdrive system, applying the original DDPG may cause the agent to search in a wrong direction, leading to a sub-optimal solution. Therefore, we do not consider the torque distribution method, based on the original DDPG, in this section. In each case, the KA-DDPG was trained for 5000 episodes. During training, the vehicle state was randomly initialized in each episode. To verify the stability of the convergence, the training in each case was repeated five times. Figure 13 shows the total rewards during the learning process of the first case, which is the case of KA-DDPG with both of the assisted learning methods. As shown in Figure 13a, the rewards converged quickly and smoothly in each learning process. Such learning processes indicate that the agent can still stably learn a reasonable torque distribution strategy, to ensure the control performance. With the assistance of knowledge-assisted learning methods, the agent no longer randomly generates actions with low rewards during the learning process, and has the ability to rapidly increase the cumulative rewards, which not only accelerated the convergence of the learning process, but also ensured that the convergence was smooth. As shown in Figure 13b, on average, the KA-DDPG-based torque distribution method outperformed the baseline in cumulative rewards after training for 200 episodes, which means that KA-DDPG not only learns control policies from the  
Figure 12: Quantitative evaluation results: (**a**) evaluation in the straight scenario and (**b**) evaluation in the cornering scenario. low-fidelity controller, but also explores and learns policies for better control performance. At about 300 episodes, each learning process converged smoothly. Then, we trained the KA-DDPG, including only the guiding reward method and without the criteria action method, in order to illustrate the contribution of the criteria action method. Figure 14 shows the learning process of the KA-DDPG, without the assistance of the criteria action method. Without the assistance of criteria actions, execution actions were searched randomly at the beginning of the learning process, making the rewards more volatile. As the number of episodes increased, the volatility of the total rewards decreased, and the learning process converged stably. Figure 14 shows the total rewards in each learning process, and Figure 14 shows the average of the total rewards. Without the assistance of the criteria action method, the KA-DDPG needed about 800 episodes to perform better than the baseline, while the first case (including both assisted learning methods) only took about 200 episodes to achieve the same result. Similarly, the convergence of KA-DDPG, without the criteria action method, was, also, slower than the first case, taking about 1200 episodes to converge. Such results illustrate that the learning cost of KA-DDPG is greatly increased without the assistance of the criteria action method. The above simulation results demonstrate that the criteria action method reduces the learning cost, by helping the agent to reduce the randomly selected actions in the learning process. Figure 13: The learning process of KA-DDPG with criteria action and guiding reward methods: (**a**) total rewards for each learning process and (**b**) the average value of the total rewards. Finally, we trained the KA-DDPG, including only the criteria action method, in order to illustrate the contribution of the guiding reward method.