  Navigation problems under unknown varying conditions are among the most
important and well-studied problems in the control field. Classic model-based
adaptive control methods can be applied only when a convenient model of the
plant or environment is provided. Recent model-free adaptive control methods
aim at removing this dependency by learning the physical characteristics of the
plant and/or process directly from sensor feedback. Although there have been
prior attempts at improving these techniques, it remains an open question as to
whether it is possible to cope with real-world uncertainties in a control
system that is fully based on either paradigm. We propose a conceptually simple
learning-based approach composed of a full state feedback controller, tuned
robustly by a deep reinforcement learning framework based on the Soft
Actor-Critic algorithm. We compare it, in realistic simulations, to a
model-free controller that uses the same deep reinforcement learning framework
for the control of a micro aerial vehicle under wind gust. The results indicate
the great potential of learning-based adaptive control methods in modern
dynamical systems.
