To achieve their goal, the researchers first defined a reward function that formulated the 'racing problem' as a minimum-time problem, as well as outlining a neural network policy that directly mapped input observations to car control commands. To perform well at Gran Turismo Sport, the controller should be trying to minimize the amount of time in which it can complete a given track.
The key objective of the recent study carried out by Song and his colleagues was to develop an artificial neural network (ANN)-based controller that can autonomously move a race car within in a simulated track, without requiring any prior knowledge of the car's dynamics.
To address these challenges and advance the frontier, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport, which is known for its detailed physics simulation of various cars and tracks." "Autonomous car racing, where the goal is to complete a given course in minimal time, features some of these difficulties of controlling a car close to its physical limitations. "Autonomous driving at high speed is a challenging task that requires generating fast and precise actions even when the vehicle is approaching its physical limits," Yunlong Song, one of the researchers who carried out the study, told TechXplore. Their findings, presented in a paper pre-published on arXiv, further highlight the potential of deep learning techniques for controlling cars in simulated environments. Researchers at University of Zurich and SONY AI Zurich have recently tested the performance of a deep reinforcement learning-based approach that was trained to play Gran Turismo Sport, the renowned car racing video game developed by Polyphony Digital and published by Sony Interactive Entertainment.