We propose a sim-to-real transfer technique utilizing a generative adversarial model CycleGAN for virtual-to-real image style transformation, reducing the gap between pure visual self-driving model performance in simulationreality. Then, we propose a method for action smoothness by enhancing the continuity in consecutive actions. The combination of these methods successfully improves the speedstability of the physical race car.

We propose a sim-to-real transfer techniquean action smoothing method which greatly enhances the driving performance of DRL-based self-driving car racing by addressing challenges related to virtual-to-real environment differencescontrol stability.This technology was presented at workshops of two toptier conferences, ICRAIJCAI, in 2022,also achieved a remarkable accomplishment by winning the top three in the AWS DeepRacer League competition (with 150,000+ contestants).

We propose a sim-to-real transfer techniquean action smoothing method that enhances the feasibility of DRL in real-world applications, opening up possibilities for robotics, robotic arms,unmanned drone control in real-world settings. Furthermore, this technology finds applications beyond self-driving car competitions, including unmanned transport vehicles in factoriesemergency exploration in challenging environments. It offers cost-effectivefast deployment capabilities.