  We investigate drag reduction mechanisms in flows past two- and
three-dimensional cylinders controlled by surface actuators using deep
reinforcement learning. We investigate 2D and 3D flows at Reynolds numbers up
to 8,000 and 4,000, respectively. The learning agents are trained in planar
flows at various Reynolds numbers, with constraints on the available actuation
energy. The discovered actuation policies exhibit intriguing generalization
capabilities, enabling open-loop control even for Reynolds numbers beyond their
training range. Remarkably, the discovered two-dimensional controls, inducing
delayed separation, are transferable to three-dimensional cylinder flows. We
examine the trade-offs between drag reduction and energy input while discussing
the associated mechanisms. The present work paves the way for control of
unsteady separated flows via interpretable control strategies discovered
through deep reinforcement learning.
