Learning neural networks for visual servoing using evolutionary methods
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 reinforcementlearning. 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 CMAES based EANT develops very good networks for visual servoing. Their performance is significantly better than those developed by the original EANT and traditional visual servoing approaches.