  Machine learning frameworks such as Genetic Programming (GP) and
Reinforcement Learning (RL) are gaining popularity in flow control. This work
presents a comparative analysis of the two, bench-marking some of their most
representative algorithms against global optimization techniques such as
Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we
review the general framework of the model-free control problem, bringing
together all methods as black-box optimization problems. Then, we test the
control algorithms on three test cases. These are (1) the stabilization of a
nonlinear dynamical system featuring frequency cross-talk, (2) the wave
cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake
flow. We present a comprehensive comparison to illustrate their differences in
exploration versus exploitation and their balance between `model capacity' in
the control law definition versus `required complexity'. We believe that such a
comparison paves the way toward the hybridization of the various methods, and
we offer some perspective on their future development in the literature on flow
control problems.
