## 1 Introduction  
Due to their advantages of simple structure, sustainability, and flexible control methods, skid-steering vehicles have been widely used in civil and military applications, including agricultural vehicles [1], combat vehicles [2; 3], robots [4; 5], and so on. As shown in Figure 1, skid-steering vehicles are typically equipped with four independent driving wheels, forming an overdrive system with more control output than control input. Unlike car-like vehicles [6; 7; 8], skid-steering vehicles do not possess a dedicated steering system; therefore, a reasonable torque distribution is key to skid-steering vehicle control. With the development of optimal problem solvers, through reinforcement learning (RL) techniques in complex systems, new insights have been provided for coordinated torque distribution in skid-steering vehicles. Therefore, the development of torque distribution strategies based on RL has become a promising research direction for future skid-steering vehicles. The conventional control methods for skid-steering vehicles, mainly, include kinematics and dynamics methods [9]. In kinematics methods, the desired control value is transformed into a reference speed for each wheel, based on the vehicle kinematics, following which each driving wheel generates driving torque, to track its own reference speed for vehicle control [10; 11; 12]. Kinematics methods are subject to significant slippage and skidding, when precisely following the wheel reference speed, which seriously impacts the control performance of the vehicle. In [13], the authors proposed a speed-adaptive control algorithm for six-wheeled differential steering vehicles. To enhance the vehicle handing and lateral stability performance, reference wheel speeds are, individually, generated for each wheel, based on their respective slipping and skidding status. In [14], a hierarchical controller was designed for a skid-steering vehicle, based on the need of dynamic control. In the controller, a reference wheel speed generator was designed to calculate the wheel speed, and a wheel speed follower is used to reach the target wheel speed. In [15], in order to mitigate the effects of wheel slip on accurate control, the authors proposed a new kinematics model for skid-steering vehicles, which has the ability to predict and compensate for the slippages in the forward kinematics. To improve maneuverability and stability, recent studies have proposed dynamics methods for the control of skid-steering vehicles. Dynamics methods can address the problem of torque distribution for skid-steering vehicles, through optimization theory [16; 17; 18]. In [19], the authors proposed a hierarchical torque distribution strategy for a skid-steering electric unmanned ground vehicle (EUGV), in order to control the longitudinal speed and yaw rate. The objective function, consisting of the longitudinal tire workload rates and weight factors of the tires, was established by considering inequality constraints, including actuator, road adhesion, and tire friction circle constraints. In [20], a hierarchical control framework for a six-wheel independent drive (6 WID) vehicle was proposed, and optimization theory was employed to distribute the driving torques. The control strategy was used to realize the real-time torque distribution, having the ability to maintain wheel failure tolerance and limit wheel slip. To date, RL algorithms have been successfully implemented in robots [21] and unmanned aerial vehicles (UAVs) [22; 23] as well as energy [24], transportation [25], and other complex systems [26; 27]. The DDPG algorithm has been successfully used to deal with decision-making problems involving continuous action spaces in various vehicle control applications [28], such as trajectory planning [29], automatic lane changing [30], and optimal torque distribution [31; 32; 33]. To overcome the shortcomings of the original DDPG in the training process, many learning tricks have been proposed to make the training more efficient and the convergence more stable.