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In this work we compare different drag-reduction strategies that computetheir actuation based on the fluctuations at a given wall-normal location inturbulent open channel flow. In order to perform this study, we implement anddescribe in detail the reinforcement-learning interface to acomputationally-efficient, parallelized, high-fidelity solver for fluid-flowsimulations. We consider opposition control (Choi, Moin, and Kim, Journal ofFluid Mechanics 262, 1994) and the policies learnt using deep reinforcementlearning (DRL) based on the state of the flow at two inner-scaled locations($y^+ = 10$ and $y^+ = 15$). By using deep deterministic policy gradient (DDPG)algorithm, we are able to discover control strategies that outperform existingcontrol methods. This represents a first step in the exploration of thecapability of DRL algorithm to discover effective drag-reduction policies usinginformation from different locations in the flow.

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