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We propose a hybrid approach fusing first principles and learning to model quadrotors and their aerodynamic effects with unprecedented accuracy. The proposed approach leverages the short-term predictive optimization capabilities of MPC with the exploratory and end-to-end training properties of RL. The vehicle reaches speeds of up to 3 meters per second and angular velocities of up to degrees per second, with accelerations of up to 1.