  Approximating model predictive control (MPC) policy using expert-based
supervised learning techniques requires labeled training data sets sampled from
the MPC policy. This is typically obtained by sampling the feasible state-space
and evaluating the control law by solving the numerical optimization problem
offline for each sample. Although the resulting approximate policy can be
cheaply evaluated online, generating large training samples to learn the MPC
policy can be time consuming and prohibitively expensive. This is one of the
fundamental bottlenecks that limit the design and implementation of MPC policy
approximation. This technical note aims to address this challenge, and proposes
a novel sensitivity-based data augmentation scheme for direct policy
approximation. The proposed approach is based on exploiting the parametric
sensitivities to cheaply generate additional training samples in the
neighborhood of the existing samples.
