Abstract In this work we propose an approach for generalization in continuous domain Reinforcement Learning that, instead of using a single function approximator, tries many different function approximators in parallel, each one de?ned in a different region of the domain. Associated with each approximator is a relevance function that locally quanti?es the quality of its approximation, so that, at each input point, the approximator with highest relevance can be selected. The relevance function is de?ned using parametric estimations of the variance of the q-values and the density of samples in the input space, which are used to quantify the accuracy and the con?dence in the approximation, respectively. These parametric estimations are obtained from a probability density distribution represented as a Gaussian Mixture Model embedded in the input-output space of each approximator. In our experiments, the proposed approach required a lesser number of experiences for learning and produced more stable convergence pro?les than when using a single function approximator.