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This paper considers intelligent data center cooling control via the Deep Reinforcement Learning (DRL) approach to improve data center sustainability. Existing DRL-based controllers are trained with a simplified data hall thermodynamic model which assumes uniform room temperature distribution. This assumption is not valid for a real-world data center with highly nonuniform temperature distribution. Furthermore, most of them cannot guarantee thermal safety during the DRL learning process. To bridge these gaps, we propose LyaSafe, a model-assisted safe DRL approach for data center cooling control. To address the safety evaluation issue, we develop a coupled model that combines a differentiable surrogate data hall thermodynamics model with the energy model. It can simulate both data hall temperature distribution and the facility energy consumption. To address safe learning, we introduce a novel …