Tóth, Szilárd Hunor and Bárdos, Ádám and Viharos, Zsolt János (2025) Adversarial Reinforcement Learning for Circular Autonomous Drifting Under Drivetrain Uncertainty. In: 2025 IEEE Intelligent Vehicles Symposium (IV). IEEE Intelligent Vehicles Symposium . IEEE, Piscataway (NJ), pp. 2341-2347. ISBN 9798331538033 10.1109/IV64158.2025.11097629

Text TothHSZ_2341_36284898_z.pdf Restricted to Registered users only Request a copy

Abstract

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.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Research Laboratory on Engineering & Management Intelligence
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	23 Jan 2026 08:03
Last Modified:	23 Jan 2026 08:03
URI:	https://eprints.sztaki.hu/id/eprint/11085

Update Item