We are releasing a MATLAB implementation of the GrouPS algorithm for sparse latent space policy search. GrouPS combines reinforcement learning and dimensionality reduction, while also including prior structural knowledge about the task. The algorithm exploits these properties in order to (1) perform efficient policy search, (2) infer the low-dimensional latent space of the task, and (3) incorporate prior structural information. Prior knowledge about locality of synergies can be included by specifying distinct groups of correlated sub-components.  The provided code includes examples for performing policy search using the V-REP robotics simulator.