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 defined in a different
region of the domain. Associated with each
approximator is a relevance function that locally quantifies the quality of its approximation, so that, at each input point, the approximator with highest relevance can be selected. The relevance function
is defined 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 confidence 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 profiles than when
using a single function approximator.
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