カリキュラム強化学習によるGran Turismo Sportでの自律追い越し
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning
Gran Turismo Sportのレース環境でカリキュラム強化学習を用い、ゲーム内AIを上回り熟練ドライバーに匹敵する追い越し制御を実現した。
著者: Yunlong Song, HaoChih Lin, Elia Kaufmann, Peter Duerr, Davide Scaramuzza
分類: cs.RO, cs.AI
原文アブストラクト
Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics, and cannot leverage the large volume of data generated by simulation or real-world driving. To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem. We evaluate our approach in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks. By leveraging curriculum learning, our approach leads to faster convergence as well as increased performance compared to vanilla reinforcement learning. As a result, the trained controller outperforms the built-in model-based game AI and achieves comparable overtaking performance with an experienced human driver.
