Machine-learning techniques have been widely applied for solving decision-making problems. Machine-learning algorithms perform better as compared to other algorithms while dealing with complex environments. The recent development in the area of neural network has enabled reinforcement learning techniques to provide the optimal policies for sophisticated and capable agents. In this paper, we would like to explore some algorithms people have applied recently based on interaction of multiple agents and their components. We would like to provide a survey of reinforcement-learning techniques to solve complex and real-world scenarios.


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    Titel :

    Revisited: Machine Intelligence in Heterogeneous Multi-Agent Systems


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Jing, Zhongliang (Herausgeber:in) / Borah, Kaustav Jyoti (Autor:in) / Talukdar, Rajashree (Autor:in)

    Kongress:

    International Conference on Aerospace System Science and Engineering ; 2019 ; Toronto, ON, Canada July 30, 2019 - August 01, 2019



    Erscheinungsdatum :

    01.03.2020


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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