Safety limits the application of traditional reinforcement learning (RL) methods to autonomous driving. To address the challenge of safe exploration in autonomous driving tasks, a novel safe RL method called Twin Delayed Deep Deterministic Policy Gradient based on Approximate Safe Action (TD3-ASA) is proposed in this paper. In TD3-ASA, the action output by the current policy during the exploration process is modified to obtain an approximate safe action, and then the approximate safe action is utilized to train a safe policy for deployment. TD3-ASA offers several advantages: 1) TD3-ASA is sample efficient and does not need any prior knowledge; 2) TD3-ASA enhances safety both during training and deployment; 3) TD3-ASA introduces an adjustable safety correction factor that enables a tradeoff between exploration and safety. Experimental results conducted on both the MetaDrive and SpeedLimit autonomous driving test platforms demonstrate the effectiveness of TD3-ASA. TD3-ASA exhibits more than triple safety during training on MetaDrive compared to the current state-of-the-art RL method, achieving a high success rate and low deployment risk.


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    Titel :

    Autonomous Driving Based on Approximate Safe Action


    Beteiligte:
    Wang, Xuesong (Autor:in) / Zhang, Jiazhi (Autor:in) / Hou, Diyuan (Autor:in) / Cheng, Yuhu (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.12.2023


    Format / Umfang :

    1228671 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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