  We introduce mlOSP, a computational template for Machine Learning for Optimal
Stopping Problems. The template is implemented in the R statistical environment
and publicly available via a GitHub repository. mlOSP presents a unified
numerical implementation of Regression Monte Carlo (RMC) approaches to optimal
stopping, providing a state-of-the-art, open-source, reproducible and
transparent platform. Highlighting its modular nature, we present multiple
novel variants of RMC algorithms, especially in terms of constructing
simulation designs for training the regressors, as well as in terms of machine
learning regression modules. Furthermore, mlOSP nests most of the existing RMC
schemes, allowing for a consistent and verifiable benchmarking of extant
algorithms. The article contains extensive R code snippets and figures, and
serves as a vignette to the underlying software package.
