Yibing Zhou

Multi-Objective Optimization for Data Center HVAC Systems Based on Edge–Cloud Collaborative Deep Reinforcement Learning

This paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control that integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization, a three-time-scale edge–cloud architecture, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy.