Model Learning

Abstract

This chapter discusses several aspects concerning the simultaneous learning of controller and internal model. We start with discussing the bootstrapping dilemma arising in this context and the consequences of insufficient sampling. It appears that homeokinetic learning solves these problems naturally, which we illustrate in several examples. Further, we extend the implementation of the internal model by a sensor-branch. This is seen to increase the applicability of the homeokinetic controller because it allows for situations where the sensor values are subject to an action-independent dynamics. The extended model is prone to an ambiguity in the learning process, which can lead to instabilities. The problem can be resolved if the time-loop error is used as an additional objective for the model learning.