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Learning Neural Networks for Visual Servoing Using Evolutionary Methods
2006
2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)
In this article we introduce a method to learn neural networks that solve a visual servoing task. Our method, called EANT, Evolutionary Acquisition of Neural Topologies, starts from a minimal network structure and gradually develops it further using evolutionary reinforcement learning. We have improved EANT by combining it with an optimisation technique called CMA-ES, Covariance Matrix Adaptation Evolution Strategy. Results from experiments with a 3 DOF visual servoing task show that the new
doi:10.1109/his.2006.264889
fatcat:v4jus2bidnhjtc7pxqbuizd6ei