  This paper presents a novel trajectory tracker for autonomous quadrotor
navigation in dynamic and complex environments. The proposed framework
integrates a distributional Reinforcement Learning (RL) estimator for unknown
aerodynamic effects into a Stochastic Model Predictive Controller (SMPC) for
trajectory tracking. Aerodynamic effects derived from drag forces and moment
variations are difficult to model directly and accurately. Most current
quadrotor tracking systems therefore treat them as simple `disturbances' in
conventional control approaches. We propose Quantile-approximation-based
Distributional Reinforced-disturbance-estimator, an aerodynamic disturbance
estimator, to accurately identify disturbances, i.e., uncertainties between the
true and estimated values of aerodynamic effects. Simplified Affine Disturbance
Feedback is employed for control parameterization to guarantee convexity, which
we then integrate with a SMPC to achieve sufficient and non-conservative
control signals. We demonstrate our system to improve the cumulative tracking
errors by at least 66% with unknown and diverse aerodynamic forces compared
with recent state-of-the-art. Concerning traditional Reinforcement Learning's
non-interpretability, we provide convergence and stability guarantees of
Distributional RL and SMPC, respectively, with non-zero mean disturbances.
