  I develop a numerical algorithm for stochastic impulse control in the spirit
of Regression Monte Carlo for optimal stopping. The approach consists in
generating statistical surrogates (aka functional approximators) for the
continuation function. The surrogates are recursively trained by empirical
regression over simulated state trajectories. In parallel, the same surrogates
are used to learn the intervention function characterizing the optimal impulse
amounts. I discuss appropriate surrogate types for this task, as well as the
choice of training sets. Case studies from forest rotation and irreversible
investment illustrate the numerical scheme and highlight its flexibility and
extensibility. Implementation in \texttt{R} is provided as a publicly available
package posted on GitHub.
