To develop a supply chain management (SCM) system that performs optimally for both each entity in the chain and the entire chain, a multi-agent reinforcement learning (MARL) technique has been developed. To solve two problems of the MARL for SCM (building a Markov decision processes for a supply chain and avoiding learning stagnation in a way similar to the "prisoner's dilemma"), a learning management method with deep-neural-network (DNN)-weight evolution (LM-DWE) has been developed. By using a beer distribution game (BDG) as an example of a supply chain, experiments with a four-agent system were performed. Consequently, the LM-DWE successfully solved the above two problems and achieved 80.0% lower total cost than expert players of the BDG.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Deep Multi-Agent Reinforcement Learning using DNN-Weight Evolution to Optimize Supply Chain Performance


    Beteiligte:
    Fuji, Taiki (Autor:in) / Ito, Kiyoto (Autor:in) / Matsumoto, Kohsei (Autor:in) / Yano, Kazuo (Autor:in)

    Erscheinungsdatum :

    2018-01-03



    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    006 / 629






    Multi-Agent Deep Reinforcement Learning in Vehicular OCC

    Islam, Amirul / Musavian, Leila / Thomos, Nikolaos | IEEE | 2022


    DEEP REINFORCEMENT LEARNING FOR MULTI-AGENT AUTONOMOUS SATELLITE INSPECTION

    Lei, Henry H. / Shubert, Matt / Damron, Nathan et al. | Springer Verlag | 2024


    Autonomous Separation Assurance with Deep Multi-Agent Reinforcement Learning

    Brittain, Marc W. / Yang, Xuxi / Wei, Peng | AIAA | 2021