Simulator-based reinforcement learning for data center cooling optimization

What this post added

This post details Meta's application of simulator-based reinforcement learning (RL) to optimize data center cooling. It explains how RL models the cooling control system as a sequential state machine, using environmental variables as states and control setpoints (e.g., supply airflow) as actions. The approach uses a physics-based simulator to train the RL agent offline, exploring potential actions and their rewards to learn an optimal policy. This has led to an average reduction of 20% in supply fan energy consumption and 4% in water usage in pilot regions, while maintaining data center temperature conditions within specifications. The post also highlights the applicability of this methodology to future AI-optimized data center designs.