In the paper, Neural Q Learning algorithm (NQL) was involved to solve the obstacle avoidance problem for UAV path planning. Q learning was good at online learning and BP network provided excellent function approximation. The combination of two methods can provided UAV a collision-free trajectory in unknown environment. Through several simulations, the proposed algorithm could gain better performance and gain higher success rate than classic Q-learning (CQL). Besides, this method was extended for deep reinforcement learning, such as DQN, which is more suitable for practical applications.


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    Titel :

    Neural Q Learning Algorithm based UAV Obstacle Avoidance


    Beteiligte:
    Zhou, Benchun (Autor:in) / Wang, Weihong (Autor:in) / Wang, Zhifeng (Autor:in) / Ding, Baoyang (Autor:in)


    Erscheinungsdatum :

    01.08.2018


    Format / Umfang :

    181825 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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