  Unmanned Aerial Vehicles (UAVs), or drones, have recently been used in
several civil application domains from organ delivery to remote locations to
wireless network coverage. These platforms, however, are naturally unstable
systems for which many different control approaches have been proposed.
Generally based on classic and modern control, these algorithms require
knowledge of the robot's dynamics. However, recently, model-free reinforcement
learning has been successfully used for controlling drones without any prior
knowledge of the robot model. In this work, we present a framework to train the
Soft Actor-Critic (SAC) algorithm to low-level control of a quadrotor in a
go-to-target task. All experiments were conducted under simulation. With the
experiments, we show that SAC can not only learn a robust policy, but it can
also cope with unseen scenarios. Videos from the simulations are available in
https://www.youtube.com/watch?v=9z8vGs0Ri5g and the code in
https://github.com/larocs/SAC_uav.
