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在IoRT网络中的移动意识资源分配,用于灾后通信,具有参数化的强化学习

Homayun Kabir1, Mau-Luen Tham1, Yoong Choon Chang1

  • 1Department of Electrical and Electronic Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Sungai Long Campus, Kajang 43000, Malaysia.

Sensors (Basel, Switzerland)
|July 29, 2023
PubMed
概括

本研究引入了一种新算法,即多通道深度Q网络 (MP-DQN),以优化灾后网络中用户设备的能源效率和吞吐量. 在自然灾害期间,MP-DQN算法增强了机器人物体的通信服务.

关键词:
深度强化学习 (DRL) 是一种深度强化学习.机器人物联网 (IoRT) 的互联网.可移动和可部署的资源单位 (MDRU)多通道深度 Q 网络 (MP-DQN)参数化的行动空间.灾难后的沟通 灾难后的沟通

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