An experimental validation of reinforcement learning applied to the position control of UAVs

In this paper, we explore the application of Reinforcement Learning (RL) to the derivation of control laws for the flight control of an unmanned aerial vehicle (UAV). The controllers are derived off-line with a simulation and the solutions are ported to an actual aircraft. Experimental results showed that the controllers stabilize the quad-rotor during the path tracking as has been learned in the simulation.