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Expert Human-Level Driving in Gran Turismo Sport Using Deep Reinforcement Learning with Image-based Representation
Ryuji Imamura · Takuma Seno · Kenta Kawamoto · Michael Spranger
Event URL: https://openreview.net/forum?id=qIRxzy3RteG »

When humans play virtual racing games, they use visual environmental information on the game screen to understand the rules within the environments. In contrast, a state-of-the-art realistic racing game AI agent that outperforms human players does not use image-based environmental information but the compact and precise measurements provided by the environment. In this paper, a vision-based control algorithm is proposed and compared with human player performances under the same conditions in realistic racing scenarios using Gran Turismo Sport (GTS), which is known as a high-fidelity realistic racing simulator. In the proposed method, the environmental information that constitutes part of the observations in conventional state-of-the-art methods is replaced with feature representations extracted from game screen images. We demonstrate that the proposed method performs expert human-level vehicle control under high-speed driving scenarios even with game screen images as high-dimensional inputs. Additionally, it outperforms the built-in AI in GTS in a time trial task, and its score places it among the top 10\% approximately 28,000 human players.

Author Information

Ryuji Imamura (Sony AI Inc.)
Takuma Seno (Sony AI)
Kenta Kawamoto (Sony AI Inc.)
Michael Spranger (Sony)

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