  The control efficacy of classical periodic forcing and deep reinforcement
learning (DRL) is assessed for a turbulent separation bubble (TSB) at
$Re_\tau=180$ on the upstream region before separation occurs. The TSB can
resemble a separation phenomenon naturally arising in wings, and a successful
reduction of the TSB can have practical implications in the reduction of the
aviation carbon footprint. We find that the classical zero-net-mas-flux (ZNMF)
periodic control is able to reduce the TSB by 15.7%. On the other hand, the
DRL-based control achieves 25.3% reduction and provides a smoother control
strategy while also being ZNMF. To the best of our knowledge, the current test
case is the highest Reynolds-number flow that has been successfully controlled
using DRL to this date. In future work, these results will be scaled to
well-resolved large-eddy simulation grids. Furthermore, we provide details of
our open-source CFD-DRL framework suited for the next generation of exascale
computing machines.
