Autonomous vehicle control has undergone remarkable developments in recent years, especially in maneuvering at the limits of traction. These developments promise improved maneuverability and safety, but they also highlight a constant challenge: translating control strategies developed in simulation into robust, real-world applications. The complexity of real-world environments, with their inherent uncertainties and rapid changes, poses significant obstacles for autonomous systems that need to dynamically adapt to unpredictable conditions, such as varying traction. The aim of this research is to investigate the effectiveness of robust adversarial reinforcement learning (RARL) for controlling circular drift maneuvers under dynamic road adhesion changes and uncertainties. The presented simulation results show that agents trained with RARL can enhance agents developed using only standard reinforcement learning techniques, where they were most critically vulnerable, such as sudden significant loss of traction during the drift initiation phase. This could present another step towards the application of more robust autonomous systems.