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    • Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
    Authors: Tianxu, Li; Kun, Zhu; Nguyen, Cong Luong; Dusit, Niyato; Qihui, Wu; Yang, Zhang; Bing, Chen;  Advisor: -;  Co-Author: - (2022)

    Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competition...
    • Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey
    Authors: Tianxu, Li; Kun, Zhu; Nguyen, Cong Luong; Dusit, Niyato;  Advisor: -;  Co-Author: - (2022)

    Future Internet involves several emerging technologies such as 5G and beyond 5G networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of Things (IoTs). Moreover, the future Internet becomes heterogeneous and decentralized with a large number of involved network entities. Each entity may need to make its local decision to improve the network performance under dynamic and uncertain network environments. Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal decision-making policy adaptively through interacting with the unknown environments. However, such an algorithm fails to model the cooperations or competition...