This paper proposes an improved deep reinforcement learning algorithm, which uses meta-learning pre-training and a new exploration mechanism to accelerate the convergence in large-scale trajectory planning problems. The algorithm can eliminate neural network non-convergence caused by the excessive planning range and has high portability in different flight scenarios.


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    Title :

    The Trajectory Planning Method for UAV in Large Airspace Based on Deep Reinforcement Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Wu, Meiping (editor) / Niu, Yifeng (editor) / Gu, Mancang (editor) / Cheng, Jin (editor) / Hu, Weijun (author) / Quan, Jiale (author) / Ma, Xianlong (author) / Gao, Zhiqiang (author)

    Conference:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Publication date :

    2022-03-18


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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