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

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