The work presented in this master thesis project is related to the control aspects of fast and agile drone trajectory tracking. Aerodynamic forces make quadrotors trajectory tracking at high-speed extremely challenging. At high speeds these complex effects have a major impact in performance loss, measured in terms of large position tracking errors. Model Predictive Control (MPC) together with Reinforcement Learning (RL) is used to tackle the problem. We propose to use RL to offline tune the MPC formulation using the data obtained from the system. MPC is an optimal control method with a well-established theory that exploits a dynamic model of the platform and provides constraint satisfaction.