  This paper focuses on a drag-reducing control strategy on a 2D-simulated
laminar flow past a cylinder. Deep reinforcement learning algorithms have been
implemented to discover efficient control schemes, using two synthetic jets
located on the cylinder's poles as actuators and pressure sensors in the wake
of the cylinder as feedback observation. The present work focuses on the
efficiency and robustness of the identified control strategy and introduces a
novel algorithm (S-PPO-CMA) to optimise the sensor layout. An energy-efficient
control strategy reducing drag by 18.4% at Reynolds number 120 is obtained.
This control policy is shown to be robust both to the Reynolds number in the
range [100,216] and to measurement noise, enduring signal to noise ratios as
low as 0.2 with negligible impact on performance. Along with a systematic study
on sensor number and location, the proposed sparsity-seeking algorithm has
achieved a successful optimisation to a reduced 5-sensor layout while keeping
state-of-the-art performance. These results highlight the interesting
possibilities of reinforcement learning for active flow control and pave the
way to efficient, robust and practical implementations of these control
techniques in experimental or industrial systems.
