Adversarial Reinforcement Learning for Circular Autonomous Drifting Under Drivetrain Uncertainty

Although significant progress has been made in autonomous vehicle control, particularly in maneuvering beyond traction limits using methods like reinforcement learning, the critical challenge of applying these closed-loop control techniques to real-world conditions persists and necessitates further research. To advance this field, the primary objective of this paper is to explore an approach for reinforcement learning-based self-driving agents to perform circular drift maneuvers under rapidly and uncertainly changing environmental conditions. The agents were trained in simulation using robust adversarial reinforcement learning (RARL) to improve robustness against significant disturbances in the dynamics of the drivetrain. Agents trained with RARL turned out to be superior to those trained without this technique, providing less uncertainty when exposed to such disturbances.