Drifting is a complicated task for autonomous vehicle control. Most
traditional methods in this area are based on motion equations derived by the
understanding of vehicle dynamics, which is difficult to be modeled precisely.
We propose a robust drift controller without explicit motion equations, which
is based on the latest model-free deep reinforcement learning algorithm soft
actor-critic. The drift control problem is formulated as a trajectory following
task, where the errorbased state and reward are designed. After being trained
on tracks with different levels of difficulty, our controller is capable of
making the vehicle drift through various sharp corners quickly and stably in
the unseen map. The proposed controller is further shown to have excellent
generalization ability, which can directly handle unseen vehicle types with
different physical properties, such as mass, tire friction, etc.

本研究采用最新的深度强化学习算法（软演员 - 评论家），提出了一种不需要明确运动方程的强健漂移控制器，通过对不同难度的赛道进行训练，使车辆能够快速、稳定地漂移通过各种锐利的拐角，控制器被证明具有良好的泛化能力，可以直接处理具有不同物理特性的不同类型的车辆。