Discovering drag reduction strategies in wall-bounded turbulent flows using deep reinforcement learning

The control of turbulent fluid flows represents a problem in several engineering applications. The chaotic, high-dimensional, non-linear nature of turbulence hinders the possibility to design robust and effective control strategies. In this work, we apply deep reinforcement learning to a three-dimensional turbulent open-channel flow, a canonical flow example that is often used as a study case in turbulence, aiming to reduce the friction drag in the flow. By casting the fluid-dynamics problem as a multi-agent reinforcement-learning environment and by training the agents using a location-invariant deep deterministic policy gradient algorithm, we are able to obtain a control strategy that achieves a remarkable 30\% drag reduction, improving over previously known strategies by about 10 percentage points.