A novel intelligent controller selection method for quadrotor attitude and altitude control is presented that maintains performance in different regimes of the flight envelope. Conventional quadrotor controllers can behave insufficiently during aggressive manoeuvring, in extreme angles the quadrotor is unable to maintain height which may result in loss of performance. By implementing several controllers designed specifically for these more extreme manoeuvres it is possible to maintain performance while expanding the flight envelope beyond conventional limitations. The method proposed uses Q-Learning to learn which controller performs best in different scenarios. The controllers that can be used consist of a low angle Nonlinear Dynamic Inversion (NDI) controller and a specialised high angle NDI controller that tries to prevent actuator saturation by controlling height through the pitch and roll angles. The algorithm is split into 2 parts. During the off-line training phase the quadrotor learns a baseline policy that can be used on-line to skip the initial exploration phase. The resulting policy is a clear representation of controller selection throughout the flight envelope. The success of on-line implementation is highly dependent on the quality of the simulation model. In the on-line phase the policy is mostly exploited and learning is done at a much lower rate. Finally it is shown that reinforcement learning is a very effective way to tune complex systems. The complex system dynamics do not need to be known, only important performance parameters need to be correctly formulated in a reward function.