Deep Reinforcement Learning achieves flow control of the 2D Karman Vortex Street

Abstract

The Karman Vortex Street has been investigated for over a century and offers a reference case for investigation of flow stability and control of high dimensionality, non-linear systems. Active flow control, while of considerable interest from a theoretical point of view and for industrial applications, has remained inaccessible due to the difficulty in finding successful control strategies. Here we show that Deep Reinforcement Learning can achieve a stable active control of the Karman vortex street behind a two-dimensional cylinder. Our results show that Deep Reinforcement Learning can be used to design active flow controls and is a promising tool to study high dimensionality, non-linear, time dependent dynamic systems present in a wide range of scientific problems.