The development of autonomous driving is pro-pelling mobility into a new era of innovation. However, existing autonomous driving systems struggle to adapt to various drivers with diverse habits and behaviors, compared with simply catering to a few fixed driving styles. As a result, this paper combines meta-learning and reinforcement learning (RL) to propose an autonomous driving algorithm for personalized decision-making and control, i.e., MetaRL-AD algorithm. MetaRL-AD utilizes the off-policy RL to improve the sample efficiency. In addition, a training cycle sampling method is adopted to improve the stability and generalization, which significantly reduces the time to personalization. It is demonstrated that in an environment with high vehicle density, the method proposed in this paper increases the convergence speed by up to a factor of 8 without any rules or knowledge assistance. The results are available as videos at https://youtu.be/3VVHBz92xnQ.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Meta Reinforcement Learning for Autonomous Driving with Rapid Adaptation to Drivers*


    Contributors:
    Xing, Jiaming (author) / Du, Haoyang (author) / Wei, Dengwei (author) / Zhang, Xinyu (author) / Cui, Yixin (author) / Huang, Yanjun (author)


    Publication date :

    2024-09-24


    Size :

    1081184 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Autonomous Driving with Deep Reinforcement Learning

    Zhu, Yuhua / Technische Universität Dresden | SLUB | 2023


    Robust Driving Policy Learning with Guided Meta Reinforcement Learning

    Lee, Kanghoon / Li, Jiachen / Isele, David et al. | IEEE | 2023


    Robust Driving Policy Learning with Guided Meta Reinforcement Learning

    Lee, Kanghoon / Li, Jiachen / Isele, David et al. | ArXiv | 2023

    Free access

    Offline Reinforcement Learning for Autonomous Driving with Real World Driving Data

    Fang, Xing / Zhang, Qichao / Gao, Yinfeng et al. | IEEE | 2022


    Deep Reinforcement Learning with Intervention Module for Autonomous Driving

    Chi, Huicong / Wang, Ping / Wang, Chao et al. | IEEE | 2022