Autonomous navigation in structured urban environments amongst pedestrians is a challenging and less explored problem. In this work, we propose to use a deep reinforcement learning based method to solve this problem of navigation. A Deep Q-Network based agent is trained in a simulator for a typical intersection crossing setup amongst pedestrians. We propose a grid based representation as a state space input to the learning agent. With this grid based representation and our reward function the agent learns a policy capable of driving safely around pedestrians and also follow the traffic rules.


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    Titel :

    Deep Reinforcement Learning based Vehicle Navigation amongst pedestrians using a Grid-based state representation*


    Beteiligte:


    Erscheinungsdatum :

    01.10.2019


    Format / Umfang :

    667396 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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