Unmanned Aerial vehicles (UAVs) are today of widespread use around the globe for a plethora of different tasks that generally would require a pilot onboard the vehicle to drive the helicopter or the airplane for both civilian and military purposes giving rise to the cost both in financial and risk terms. The purpose of this thesis is to develop, implement and test on physical hardware a Reinforcement Learning (RL) based PID controller. RL exploits a Deep Deterministic Policy Gradient (DDPG) algorithm, which is an off-policy actor-critic method. The PID approach is performed through fine tuning and parameter estimation of the controller inner-loop gains for three circular trajectories with different duration's and velocities.