専門知識駆動型強化学習による自律レーシング：軌道誘導と動的制約の活用
Expert Knowledge-driven Reinforcement Learning for Autonomous Racing via Trajectory Guidance and Dynamics Constraints
最小曲率レーシングラインによる軌道誘導と車両動的制約を強化学習に組み込み、二段階カリキュラム学習で安定走行から高速走行へと段階的に学習する自律レーシング手法を提案した。
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著者: Bo Leng, Weiqi Zhang, Zhuoren Li, Lu Xiong, Guizhe Jin, Ran Yu, Chen Lv
分類: cs.RO
原文アブストラクト
Reinforcement learning has shown significant potential for autonomous racing, but it still faces challenges such as training instability, inefficient exploration, and unsafe action outputs in high-dynamic racing scenarios. This paper proposes a Trajectory guidance and Dynamics constraints Reinforcement Learning (TraD-RL) framework for autonomous racing. The proposed method incorporates expert prior knowledge into policy learning through Minimum Curvature Racing Line (MCRL) guidance, explicit vehicle dynamics constraints, and two-stage curriculum learning. MCRL provides global path and velocity references through observation augmentation and reward shaping, thereby improving exploration efficiency and racing performance. Yaw rate and sideslip angle constraints are introduced to characterize the vehicle dynamic safe operating envelope, and the corresponding stability costs are incorporated into policy optimization through Lagrangian relaxation. Moreover, the two-stage curriculum learning strategy enables a progressive transition from stable trajectory following to high-speed performance exploration. Experiments on two racetracks demonstrate that TraD-RL improves racing performance while maintaining a favorable balance between speed and dynamic stability. Further analyses of ablation, sensitivity, and robustness validate the effectiveness and stability of the proposed framework.
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- 速く走り、速く学ぶ：高性能自律レースのためのオンボード強化学習強化学習/自律走行
- 局所計画サブシステムを学習する自律レーシング強化学習/自律走行
