A reward-reinforced generativeadversarial network can be used as ageneric framework for multi-agent learning at the system level . We demonstrated our method isresilient and outperforms other conventional reinforcement learning methods . This method is also applied to a practical case study: maximising the number of user connections to autonomous airborne base stations in a mobile communication network . Our method maximises the data likelihood using a cost function underwhich agents have optimal learned behaviours. Our method can also be used to maximise the data likelyality of a mobile communications network . We hope to use this method to improve the efficiency and efficacy of such systems in the future of the mobile communications industry . Back to Mail Online home . Back To the page you came from . contact us on the page http://wwwwww.mailonline.com/newsquiz

Author(s) : Changgang Zheng, Shufan Yang, Juan Parra-Ullauri, Antonio Garcia-Dominguez, Nelly Bencomo

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Keywords : method - mobile - learning - network - reward -

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