  In this work, we present an approach to supervisory reinforcement learning
control for unmanned aerial vehicles (UAVs). UAVs are dynamic systems where
control decisions in response to disturbances in the environment have to be
made in the order of milliseconds. We formulate a supervisory control
architecture that interleaves with extant embedded control and demonstrates
robustness to environmental disturbances in the form of adverse wind
conditions. We run case studies with a Tarot T-18 Octorotor to demonstrate the
effectiveness of our approach and compare it against a classic cascade control
architecture used in most vehicles. While the results show the performance
difference is marginal for nominal operations, substantial performance
improvement is obtained with the supervisory RL approach under unseen wind
conditions.
