InfoMax Bayesian learning of the Furuta pendulum

Authors:
László A. Jeni;György Flórea;András Lörincz
Affiliations:
Eötvös Loránd University, Department of Software Technology and Methodology;Eötvös Loránd University, Department of Information Systems;Eötvös Loránd University, Department of Information Systems
Venue:
Acta Cybernetica
Year:
2008

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Abstract

We have studied the InfoMax (D-optimality) learning for the two-link Furuta pendulum. We compared InfoMax and random learning methods. The InfoMax learning method won by a large margin, it visited a larger domain and provided better approximation during the same time interval. The advantages and the limitations of the InfoMax solution are treated.