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When humans play virtual racing games, they use visual environmentalinformation on the game screen to understand the rules within the environments.In contrast, a state-of-the-art realistic racing game AI agent that outperformshuman players does not use image-based environmental information but thecompact and precise measurements provided by the environment. In this paper, avision-based control algorithm is proposed and compared with human playerperformances under the same conditions in realistic racing scenarios using GranTurismo Sport (GTS), which is known as a high-fidelity realistic racingsimulator. In the proposed method, the environmental information thatconstitutes part of the observations in conventional state-of-the-art methodsis replaced with feature representations extracted from game screen images. Wedemonstrate that the proposed method performs expert human-level vehiclecontrol under high-speed driving scenarios even with game screen images ashigh-dimensional inputs. Additionally, it outperforms the built-in AI in GTS ina time trial task, and its score places it among the top 10% approximately28,000 human players.

Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation