AI Racing - On learning racing policies with reinforcement learning скачать в хорошем качестве

AI Racing - On learning racing policies with reinforcement learning

In this work we dive into the topic of Reinforcement Learning controllers transferability to the real-world. We investigate how domain randomization, actuator dynamics modeling, and policy architecture influence RL controllers performance and safety. PDF: Website: https://grzegorzczput.github.io/rl-racing/ We evaluated our work on the F1TENTH race car, our RL policy not only surpasses a state-of-the-art Model Predictive Control (MPC), but, to the best of our knowledge, also represents the first instance of an RL policy outperforming expert human drivers in RC racing. This work identifies the key factors driving this performance improvement, providing critical insights for the design of robust RL-based control strategies for autonomous vehicles. AI Racing, Reinforcement Learning