Multi-task long-range autonomous driving in urban areas is a challenging task. Traditional methods are not applicable to situations with high-dimensional observations. Current reinforcement learning (RL) based algorithms are able to train agents to utilize high-dimensional signals for vehicle control and accomplish short-range tasks. However, current RL methods do not work well on multi-task long-range urban driving tasks. In this paper, we propose a novel Hierarchical-planning and Spatial-encoding-based Multi-task Reinforcement Learning (HSMRL) model for the autonomous driving problem in urban areas. For long-range tasks, global routes are planned in real-time based on the A* algorithm, and short-range tasks are constructed based on the global routes, through which the short-range tasks are regarded as sub-tasks of long-range tasks. Then the short-range task information is efficiently encoded using spatial encoding, and the short-range tasks are accomplished by RL algorithms. We also prove that under our settings, multiple autonomous driving tasks are capable of training a shared policy. Experiments show that our method can stably accomplish the multi-task long-range driving tasks and performs significantly better than the state-of-the-art methods.


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    Titel :

    Multi-Task Long-Range Urban Driving Based on Hierarchical Planning and Reinforcement Learning


    Beteiligte:
    Zhang, Xiaoqin (Autor:in) / Wu, Mouyu (Autor:in) / Ma, Huimin (Autor:in) / Hu, Tianyu (Autor:in) / Yuan, Jian (Autor:in)


    Erscheinungsdatum :

    19.09.2021


    Format / Umfang :

    1160859 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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