Traditional manipulator control requires the establishment of complex models and mathematical equations, and requires manual adjustment of parameters, which cannot adapt to tasks and environmental changes. With the rapid development of artificial intelligence, deep reinforcement learning algorithms show strong online adaptability and self-learning capabilities for complex systems, and have gradually become one of the important research hotspots in the field of intelligent control and artificial intelligence in recent years. Therefore, this paper combines the deep reinforcement learning algorithm with the manipulator control system, and uses the deep deterministic policy gradient (DDPG) algorithm to achieve the end position control of the manipulator. On this basis, this paper presents a scheme to improve the reward function. Finally, the DDPG algorithm is used for interactive training with the manipulator model. The experimental results show that the deep reinforcement learning algorithm can realize the control of the manipulator, and the improved reward function scheme can greatly improve the stability of training and the task completion rate.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Intelligent Control of Manipulator Based on Deep Reinforcement Learning


    Beteiligte:
    Zhou, Jiangtao (Autor:in) / Zheng, Hua (Autor:in) / Zhao, Dongzhu (Autor:in) / Chen, Yingxue (Autor:in)


    Erscheinungsdatum :

    16.07.2021


    Format / Umfang :

    9732003 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Closed-loop dynamic control of a soft manipulator using deep reinforcement learning

    A. Centurelli / L. Arleo / A. Rizzo et al. | BASE | 2022

    Freier Zugriff

    Tracking control of intelligent ship based on deep reinforcement learning

    Kang ZHU / Zhen HUANG / Xuming WANG | DOAJ | 2021

    Freier Zugriff

    Signal lamp intelligent control method based on deep reinforcement learning

    WEI KAI / ZHU YONG / ZHANG DONGHAI et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    Intelligent Robust Disturbance Rejection Control via Deep Reinforcement Learning

    Huang, Hanqiao / Xu, Feihong / Cheng, Haoyu et al. | British Library Conference Proceedings | 2022


    Intelligent Robust Disturbance Rejection Control via Deep Reinforcement Learning

    Huang, Hanqiao / Xu, Feihong / Cheng, Haoyu et al. | Springer Verlag | 2022