Algorithmic Implementation

Abstract

This chapter presents a unified algorithm implementing the homeokinetic learning rules including a number of extensions partly discussed already in earlier chapters of this book. We continue with some guidelines and tips on how to use the homeokinetic “brain.” We discuss techniques and special methods to make the self-supervised learning of embodied systems more reliable from the practical point of view. This includes the regularization procedures for the singularities in the time-loop error and different norms of the error for the gradient descent. The internal complexity of the controller and the model is extended by the generalization to multilayer networks. Apart from that the computational complexity of the learning algorithm will be reduced essentially by easing non-trivial matrix inversions. This is important for truly autonomous hardware realizations.