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In this work, we present an approach to supervisory reinforcement learningcontrol for unmanned aerial vehicles (UAVs). UAVs are dynamic systems wherecontrol decisions in response to disturbances in the environment have to bemade in the order of milliseconds. We formulate a supervisory controlarchitecture that interleaves with extant embedded control and demonstratesrobustness to environmental disturbances in the form of adverse windconditions. We run case studies with a Tarot T-18 Octorotor to demonstrate theeffectiveness of our approach and compare it against a classic cascade controlarchitecture used in most vehicles. While the results show the performancedifference is marginal for nominal operations, substantial performanceimprovement is obtained with the supervisory RL approach under unseen windconditions.

A Reinforcement Learning Approach for Robust Supervisory Control of UAVs Under Disturbances