The work on deep reinforcement learning DRL for active flow control by P. Varela et al. has been published in Actuators. We find significantly different control strategies for flow past a 2D circular cylinder depending on the Reynolds number (up to Re=2000). The agent, which controls two jets attached on the cylinder surface, learns to exploit the particular flow physics to reduce the overall drag.