  The present study proposes an active flow control (AFC) approach based on
deep reinforcement learning (DRL) to optimize the performance of multiple
plasma actuators on a square cylinder. The investigation aims to modify the
control inputs of the plasma actuators to reduce the drag and lift forces
affecting the cylinder while maintaining a stable flow regime. The environment
of the proposed model is represented by a two-dimensional direct numerical
simulation (DNS) of a flow past a square cylinder. The control strategy is
based on the regulation of the supplied alternating current (AC) voltage at
three distinct configurations of the plasma actuators. The effectiveness of the
designed strategy is first investigated for Reynolds number, $Re_{D} = 100$,
and further applied for $Re_{D} = 180$. The applied active flow control
strategy is able to reduce the mean drag coefficient by 97\% at $Re_{D} = 100$
and by 99\% at $Re_D=180$. Furthermore, the results from this study show that
with the increase in Reynolds number, it becomes more challenging to eliminate
vortex shedding with plasma actuators located only on the rear surface of the
cylinder. Nevertheless, the proposed control scheme is able to completely
suppress it with an optimized configuration of the plasma actuators.
