## 1 Introduction
In [34], the authors proposed a DDPG-based controller that allows UAVs to fly robustly in uncertain environments. Three learning tricks--delayed learning, adversarial attack, and mixed exploration--were introduced, to overcome the fragility and volatility of the original DDPG, which greatly improved the convergence speed, convergence effect, and stability. In [35], a control approach based on Twin Delayed DDPG (TD3) was proposed, to handle the model-free attitude control problem of On-Orbit Servicing Spacecraft (OOSS), under the guidance of a mixed reward system. The Proportional-Integral-Derivative (PID) guide TD3 algorithm effectively in  
Figure 1: Skid-steering vehicle diagram. creased the training speed and learning stability of the TD3 algorithm, through the use of prior knowledge. In [36], an improved energy management framework that embeds expert knowledge into the DDPG was proposed for hybrid electric vehicles (HEVs). By incorporating the battery characteristics and the optimal brake specific fuel consumption of HEVs, the proposed framework not only accelerated the learning process, but also obtained better fuel economy. In [37], the authors proposed a knowledge-assisted DDPG for the control of a cooperative wind farm, by combining knowledge-assisted methods with the DDPG algorithm. Three analytical models were utilized to speed up the learning process and train robustly. To summarize the above work, kinematic methods are incapable of overcoming unexpected wheel slip, while dynamics methods can, theoretically, effectively control wheel slip within a limited range. However, the implementation of dynamics methods requires complicated functions, in order to estimate the vehicle model and the wheel-ground interactions, which are difficult to apply in practice [38]. The RL algorithm iteratively explores the optimal control strategy of the system in the training process, and the associated neural networks can approximate the dynamics model and wheel-ground interactions. Therefore, an RL-based torque distribution strategy for skid-steering vehicles is incorporated into our work. In this study, we propose a KA-DDPG-based driving torque distribution method for skid-steering vehicles, in order to minimize the tracking error, with respect to the desired value. We, first, analyze a dynamics model of skid-steering vehicles for torque distribution. Then, the agent of the KA-DDPG algorithm is designed for vehicle control. Based on the torque distribution strategy of KA-DDPG, we can achieve longitudinal speed and yaw rate tracking control for skid-steering vehicles. The main contributions of this study can be summarized as follows: (1) a KA-DDPG-based torque distribution strategy is proposed for skid-steering vehicles, in order to minimize the errors of the longitudinal speed and yaw rate, thus realizing the tracking control of the desired value; (2) considering the learning efficiency of the KA-DDPG algorithm, the knowledge-assisted RL framework is proposed by combining two knowledge-assisted learning methods with the DDPG algorithm; and (3) a dynamics model for skid-steering vehicles is constructed, in order to evaluate the performance of the proposed method. The remainder of this paper is organized as follows. Section 2 consists of two parts: one addressing the dynamics model of skid-steering vehicles, and another focused on the DDPG algorithm. Section 3 presents our KA-DDPG-based torque distribution method for skid-steering vehicles. The settings of the simulation environment are detailed in Section 4. In Section 5, the performance of the KA-DDPG-based torque distribution strategy and the contributions of the assisted learning methods in KA-DDPG are illustrated. Section 6 concludes this work and discusses possible future work.