Go-Kart Racing Simulator for Reinforcement Learning with Augmented Sim2Real Adaptation

Training self-driving cars in real-world scenarios is inefficient due to the possibility of crashes with obstacles and borders. This paper introduces a virtual environment to enhance reinforcement learning training in a virtual Go-Kart racing simulator. The primary objective is to leverage augmented reality to enhance observations inside the simulation, improve policy networks, and make the Value function precise and robust. We develop the wrapper for the CARLA simulator, enabling a cost-effective Sim2Real transition. It is demonstrated that the augmented Sim2Real adaptation successfully integrates simulated training outcomes into real-world scenarios where the real Go-Kart can accomplish six laps in a single-race mode, reaching the maximum speed of 11.5 m/s.