  Deep reinforcement learning (DRL) for fluidic pinball, three individually
rotating cylinders in the uniform flow arranged in an equilaterally triangular
configuration, can learn the efficient flow control strategies due to the
validity of self-learning and data-driven state estimation for complex fluid
dynamic problems. In this work, we present a DRL-based real-time feedback
strategy to control the hydrodynamic force on fluidic pinball, i.e., force
extremum and tracking, from cylinders' rotation. By adequately designing reward
functions and encoding historical observations, and after automatic learning of
thousands of iterations, the DRL-based control was shown to make reasonable and
valid control decisions in nonparametric control parameter space, which is
comparable to and even better than the optimal policy found through lengthy
brute-force searching. Subsequently, one of these results was analyzed by a
machine learning model that enabled us to shed light on the basis of
decision-making and physical mechanisms of the force tracking process. The
finding from this work can control hydrodynamic force on the operation of
fluidic pinball system and potentially pave the way for exploring efficient
active flow control strategies in other complex fluid dynamic problems.
