  Continuous direct yaw moment control systems such as torque-vectoring
controller are an essential part for vehicle stabilization. This controller has
been extensively researched with the central objective of maintaining the
vehicle stability by providing consistent stable cornering response. The
ability of careful tuning of the parameters in a torque-vectoring controller
can significantly enhance vehicle's performance and stability. However, without
any re-tuning of the parameters, especially in extreme driving conditions e.g.
low friction surface or high velocity, the vehicle fails to maintain the
stability. In this paper, the utility of Reinforcement Learning (RL) based on
Deep Deterministic Policy Gradient (DDPG) as a parameter tuning algorithm for
torque-vectoring controller is presented. It is shown that, torque-vectoring
controller with parameter tuning via reinforcement learning performs well on a
range of different driving environment e.g., wide range of friction conditions
and different velocities, which highlight the advantages of reinforcement
learning as an adaptive algorithm for parameter tuning. Moreover, the
robustness of DDPG algorithm are validated under scenarios which are beyond the
training environment of the reinforcement learning algorithm. The simulation
has been carried out using a four wheels vehicle model with nonlinear tire
characteristics. We compare our DDPG based parameter tuning against a genetic
algorithm and a conventional trial-and-error tunning of the torque vectoring
controller, and the results demonstrated that the reinforcement learning based
parameter tuning significantly improves the stability of the vehicle.
