Simulator-based reinforcement learning for data center cooling optimization

This post details Meta's application of simulator-based reinforcement learning (RL) to optimize data center cooling airflow setpoints. The RL agent operates in a simulated environment, trained on historical data and simulated scenarios, to learn a policy that minimizes energy and water consumption while maintaining environmental specifications. The system achieved an average reduction of 20% in supply fan energy consumption and 4% in water usage in a pilot region. The physics-based simulator is crucial for training the RL model with diverse scenarios and ensuring hardware performance is not affected by temperature fluctuations.