  We present a Reinforcement Learning-based Robust Nonlinear Model Predictive
Control (RL-RNMPC) framework for controlling nonlinear systems in the presence
of disturbances and uncertainties. An approximate Robust Nonlinear Model
Predictive Control (RNMPC) of low computational complexity is used in which the
state trajectory uncertainty is modelled via ellipsoids. Reinforcement Learning
is then used in order to handle the ellipsoidal approximation and improve the
closed-loop performance of the scheme by adjusting the MPC parameters
generating the ellipsoids. The approach is tested on a simulated Wheeled Mobile
Robot (WMR) tracking a desired trajectory while avoiding static obstacles.
