PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robustness in the long run: Auto-teaching vs Anticipation in Evolutionary Robotics
Nicolas Godzik, Marc Schoenauer and Michèle Sebag
In: PPSN 2004, September 2004, Birmingham.

Abstract

In Evolutionary Robotics, auto-teaching networks, neural networks that modify their own weights during the life-time of the robot, have been shown to be powerful architectures to develop adaptive controllers. Unfortunately, when run for a longer period of time than that used during evolution, the long-term behavior of such networks can become unpredictable. This paper gives an example of such dangerous behavior, and proposes an alternative solution based on anticipation: as in auto-teaching networks, a secondary network is evolved, but its outputs try to predict the next state of the robot sensors. The weights of the action network are adjusted using some back-propagation procedure based on the errors made by the anticipatory network. First results -- in simulated environments -- show a tremendous increase in robustness of the long-term behavior of the controller.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:614
Deposited By:Marc Schoenauer
Deposited On:29 December 2004