Applying reinforcement learning (RL) algorithms to control systems design remains a challenging task due to the potential unsafe exploration and the low sample efficiency. In this paper, we propose a novel safe model-based RL algorithm to solve the collision-free model-reference trajectory tracking problem of uncertain autonomous vehicles (AVs). Firstly, a new type of robust control barrier function (CBF) condition for collision-avoidance is derived for the uncertain AVs by incorporating the estimation of the system uncertainty with Gaussian process (GP) regression. Then, a robust CBF-based RL control structure is proposed, where the nominal control input is composed of the RL policy and a model-based reference control policy. The actual control input obtained from the quadratic programming problem can satisfy the constraints of collision-avoidance, input saturation and velocity boundedness simultaneously with a relatively high probability. Finally, within this control structure, a Dyna-style safe model-based RL algorithm is proposed, where the safe exploration is achieved through executing the robust CBF-based actions and the sample efficiency is improved by leveraging the GP models. The superior learning performance of the proposed RL control structure is demonstrated through simulation experiments.


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    Titel :

    Safe Reinforcement Learning for Model-Reference Trajectory Tracking of Uncertain Autonomous Vehicles With Model-Based Acceleration


    Beteiligte:
    Hu, Yifan (Autor:in) / Fu, Junjie (Autor:in) / Wen, Guanghui (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2023


    Format / Umfang :

    2583759 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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