  This work reports the application of a model-free
deep-reinforcement-learning-based (DRL) flow control strategy to suppress
perturbations evolving in the 1-D linearised Kuramoto-Sivashinsky (KS) equation
and 2-D boundary layer flows. The former is commonly used to model the
disturbance developing in flat-plate boundary layer flows. These flow systems
are convectively unstable, being able to amplify the upstream disturbance, and
are thus difficult to control. The control action is implemented through a
volumetric force at a fixed position and the control performance is evaluated
by the reduction of perturbation amplitude downstream. We first demonstrate the
effectiveness of the DRL-based control in the KS system subjected to a random
upstream noise. The amplitude of perturbation monitored downstream is
significantly reduced and the learnt policy is shown to be robust to both
measurement and external noise. One of our focuses is to optimally place
sensors in the DRL control using the gradient-free particle swarm optimisation
algorithm. After the optimisation process for different numbers of sensors, a
specific eight-sensor placement is found to yield the best control performance.
The optimised sensor placement in the KS equation is applied directly to
control 2-D Blasius boundary layer flows and can efficiently reduce the
downstream perturbation energy. Via flow analyses, the control mechanism found
by DRL is the opposition control. Besides, it is found that when the flow
instability information is embedded in the reward function of DRL to penalise
the instability, the control performance can be further improved in this
convectively-unstable flow.
