New paper out on deep reinforcement learning for active flow control

The work on 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.