The Fusion of Deep Reinforcement Learning and Edge Computing for
  Real-time Monitoring and Control Optimization in IoT Environments
Jingyu Xu Weixiang Wan Linying Pan Wenjian Sun Yuxiang Liu
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 edge computing resources, achieving global optimization.
Results demonstrate that this approach reduces cloud-edge communication
latency, accelerates response to abnormal situations, reduces system failure
rates, extends average equipment operating time, and saves costs for manual
maintenance and replacement. This ensures real-time and stable control.