March 14, 2024, 4:42 a.m. | Jingyu Xu, Weixiang Wan, Linying Pan, Wenjian Sun, Yuxiang Liu

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07923v1 Announce Type: cross
Abstract: In response to the demand for real-time performance and control quality in industrial Internet of Things (IoT) environments, this paper proposes an optimization control system based on deep reinforcement learning and edge computing. The system leverages cloud-edge collaboration, deploys lightweight policy networks at the edge, predicts system states, and outputs controls at a high frequency, enabling monitoring and optimization of industrial objectives. Additionally, a dynamic resource allocation mechanism is designed to ensure rational scheduling of …

abstract arxiv cloud computing control cs.ai cs.lg cs.ni cs.sy demand edge edge computing eess.iv eess.sy environments fusion industrial industrial internet of things internet internet of things iot monitoring optimization paper performance quality real-time reinforcement reinforcement learning type

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