Learning Nonlinear State-Space Models for Control
Tapani Raiko and Matti Tornio
In: International Joint Conference on Neural Networks, IJCNN 2005, 31 Jul - 03 Aug 2005, Montreal, Canada.
This paper studies the learning of nonlinear state-space models for a control task. This has some advantages over traditional methods. Variational Bayesian learning provides a framework where uncertainty is explicitly taken into account and system identification can be combined with model-predictive control. Three different control schemes are used. One of them, optimistic inference control, is a novel method based directly on the probabilistic modelling. Simulations with a cart-pole swing-up task confirm that the latent state space provides a representation that is easier to predict and control than the original observation space.