  The aerodynamic design of modern civil aircraft requires a true sense of
intelligence since it requires a good understanding of transonic aerodynamics
and sufficient experience. Reinforcement learning is an artificial general
intelligence that can learn sophisticated skills by trial-and-error, rather
than simply extracting features or making predictions from data. The present
paper utilizes a deep reinforcement learning algorithm to learn the policy for
reducing the aerodynamic drag of supercritical airfoils. The policy is designed
to take actions based on features of the wall Mach number distribution so that
the learned policy can be more general. The initial policy for reinforcement
learning is pretrained through imitation learning, and the result is compared
with randomly generated initial policies. The policy is then trained in
environments based on surrogate models, of which the mean drag reduction of 200
airfoils can be effectively improved by reinforcement learning. The policy is
also tested by multiple airfoils in different flow conditions using
computational fluid dynamics calculations. The results show that the policy is
effective in both the training condition and other similar conditions, and the
policy can be applied repeatedly to achieve greater drag reduction.
