The biasing of dynamical simulations along CVs uncovered by unsupervised learning has become a standard approach in analysis of molecular systems. However, despite parallels with reinforcement learning (RL), state of the art RL methods have yet to reach the molecular dynamics community. The interaction between unsupervised learning, dynamical simulations, and RL is therefore a promising area of research. We introduce a method for enhanced sampling that uses nonlinear geometry estimated by an unsupervised learning algorithm in a reinforcement-learning enhanced sampler. We give theoretical background justifying this method, and show results on data.