Open-Ended Evolutionary Robotics: an Information Theoretic Approach
Abstract
This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach.
Keywords
Cite
@article{arxiv.1006.4959,
title = {Open-Ended Evolutionary Robotics: an Information Theoretic Approach},
author = {Pierre Delarboulas and Marc Schoenauer and Michèle Sebag},
journal= {arXiv preprint arXiv:1006.4959},
year = {2010}
}