Challenges and Opportunities of Applying Reinforcement Learning to Autonomous Racing
Peter R. Wurman, Peter Stone, Michael Spranger · IEEE Intelligent Systems · 2022
Simulated motorsports are an exciting environment in which to explore the power and limitations of deep reinforcement learning. Racing requires precise control of a vehicle that is operating at its traction limits while competing wheel-to-wheel with other drivers. We recently demonstrated an agent that can beat the best drivers in the world at the racing game Gran Turismo. In this article, we briefly discuss some of the lessons learned and some of the remaining open research challenges.