深層強化学習によるGran Turismo Sportでの超人性能
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning
高忠実度な車両シミュレーションとコース進捗報酬を用いた深層強化学習により、Gran Turismo Sportで内蔵AIを超え、5万人以上の人間プレイヤー中最速のドライバーをも上回る自動運転レーシングを実現した。
著者: Florian Fuchs, Yunlong Song, Elia Kaufmann, Davide Scaramuzza, Peter Duerr
分類: cs.AI, cs.LG, cs.RO
原文アブストラクト
Autonomous car racing is a major challenge in robotics. It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling. Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem. In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning. We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers. Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players.
