Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Stable Function Approximation in Dynamic Programming
Stable Function Approximation in Dynamic Programming
Reinforcement learning with Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
Reinforcement Learning in Continuous Time and Space
Neural Computation
Neurocomputing
Gaussian process dynamic programming
Neurocomputing
Adaptive autonomous control using online value iteration with Gaussian processes
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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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.