  We introduce a reinforcement learning (RL) environment to design and
benchmark control strategies aimed at reducing drag in turbulent fluid flows
enclosed in a channel. The environment provides a framework for
computationally-efficient, parallelized, high-fidelity fluid simulations, ready
to interface with established RL agent programming interfaces. This allows for
both testing existing deep reinforcement learning (DRL) algorithms against a
challenging task, and advancing our knowledge of a complex, turbulent physical
system that has been a major topic of research for over two centuries, and
remains, even today, the subject of many unanswered questions. The control is
applied in the form of blowing and suction at the wall, while the observable
state is configurable, allowing to choose different variables such as velocity
and pressure, in different locations of the domain. Given the complex nonlinear
nature of turbulent flows, the control strategies proposed so far in the
literature are physically grounded, but too simple. DRL, by contrast, enables
leveraging the high-dimensional data that can be sampled from flow simulations
to design advanced control strategies. In an effort to establish a benchmark
for testing data-driven control strategies, we compare opposition control, a
state-of-the-art turbulence-control strategy from the literature, and a
commonly-used DRL algorithm, deep deterministic policy gradient. Our results
show that DRL leads to 43% and 46% drag reduction in a minimal and a larger
channel (at a friction Reynolds number of 180), respectively, outperforming the
classical opposition control by around 20 percentage points.
