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We present a reinforcement learning-based solution to autonomously race on aminiature race car platform. We show that a policy that is trained purely insimulation using a relatively simple vehicle model, including modelrandomization, can be successfully transferred to the real robotic setup. Weachieve this by using novel policy output regularization approach and a liftedaction space which enables smooth actions but still aggressive race cardriving. We show that this regularized policy does outperform the Soft ActorCritic (SAC) baseline method, both in simulation and on the real car, but it isstill outperformed by a Model Predictive Controller (MPC) state of the artmethod. The refinement of the policy with three hours of real-world interactiondata allows the reinforcement learning policy to achieve lap times similar tothe MPC controller while reducing track constraint violations by 50%.

Learning from Simulation, Racing in Reality