Deep reinforcement learning towards real-world dynamic thermal management of data centers

•Algorithm can be sensitive to algorithm setting, affecting optimality and robustness.•The discrepancy between the digitized and initial objectives cannot be ignored.•The system dynamics that can affect potential performance improvement is revealed.•Algorithms can obtain energy savings and violation reductions in some scenarios.•Actor-critic, off-policy, and model-based algorithms exhibit better performance.