  In this work we compare different drag-reduction strategies that compute
their actuation based on the fluctuations at a given wall-normal location in
turbulent open channel flow. In order to perform this study, we implement and
describe in detail the reinforcement-learning interface to a
computationally-efficient, parallelized, high-fidelity solver for fluid-flow
simulations. We consider opposition control (Choi, Moin, and Kim, Journal of
Fluid Mechanics 262, 1994) and the policies learnt using deep reinforcement
learning (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 existing
control methods. This represents a first step in the exploration of the
capability of DRL algorithm to discover effective drag-reduction policies using
information from different locations in the flow.
