  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.
