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Reinforcement learning is applied to the development of control strategies inorder to reduce skin friction drag in a fully developed turbulent channel flowat a low Reynolds number. Motivated by the so-called opposition control (Choiet al. 1993), in which a control input is applied so as to cancel thewall-normal velocity fluctuation on a detection plane at a certain distancefrom the wall, we consider wall blowing and suction as a control input, and itsspatial distribution is determined by the instantaneous streamwise andwall-normal velocity fluctuations at the distance of 15 wall units above thewall. Deep neural network is used to express the complex relationship betweenthe sensing information and the control input, and it is trained so as tomaximize the expected long-term reward, i.e., drag reduction. When only thewall-normal velocity fluctuation is measured and a linear network is used, thepresent framework successfully reproduces the optimal linear weight for theopposition control reported in the previous study (Chung & Talha 2011). Incontrast, when a non-linear network is used, more complex control strategiesbased on the instantaneous streamwise and wall-normal velocity fluctuations areobtained. Specifically, the obtained control strategies abruptly switch betweenstrong wall blowing and suction for downwelling of a high-speed fluid towardthe wall and upwelling of a low-speed fluid away from the wall, respectively.The obtained control policies lead to drag reduction rates as high as 37 %,which is higher than 23 % achieved by the conventional opposition control atthe same Reynolds number. The present results indicate that reinforcementlearning can be a novel framework for the development of effective controlstrategies through systematic learning based on a large number of trials.

Reinforcement Learning of Control Strategies for Reducing Skin Friction Drag in a Fully Developed Channel Flow