Comparison of Deep Reinforcement Learning Algorithms in Data Center Cooling Management: A Case Study

The growth in scale and power density of Data Centers (DC) poses serious challenges to the cooling management. Recently, there are many studies using machine learning to solve the cooling management problems. However, a comprehensive comparative study is still missing. In this work, we compare the performance of various Deep Reinforcement Learning (DRL) algorithms, including Deep-Q Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Branching Dueling Q-Network (BDQ), using the Active Ventilation Tiles (AVTs) control problem in raised-floor DC as an example. In particular, we design two multiagent algorithms based on DQN and three critic architectures for DDPG. Simulations based on real world workload show that DDPG provides the best performance over the considered algorithms.