论文标题
高速自主漂移，深入增强学习
High-speed Autonomous Drifting with Deep Reinforcement Learning
论文作者
论文摘要
漂移是自动驾驶汽车控制的复杂任务。该领域中的大多数传统方法都是基于对车辆动力学的理解得出的运动方程，这很难准确地建模。我们提出了一个不明确运动方程的稳健漂移控制器，该控制器基于最新的无模型的深钢筋学习算法软参与者评论。漂移控制问题被提出为以下任务的轨迹，在该任务中设计了错误的状态和奖励。在经过不同水平的难度训练之后，我们的控制器能够使车辆在看不见的地图中快速而稳定地漂移各种锋利的角落。所提出的控制器进一步证明具有出色的概括能力，可以直接处理具有不同物理特性的未见车辆类型，例如质量，轮胎摩擦等。
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.
