Learning Nonlinear State-Space Models for Control

Tapani Raiko and Matti Tornio Neural Networks Research Centre
Helsinki University of Technology
P.O.Box 5400, FI-02015 TKK
Espoo, FINLAND
E-mail: tapani.raiko (a) hut.fi, matti.tornio (a) hut.fi

Abstract:

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.