Learning to predict variable-delay rewards and its role in autonomous developmental robotics

Abstract

Researchers in the new field of "developmental robotics" propose to provide robots with so-called developmental programs. Similar to the development of human infants, robots might use those programs to interact with humans and their environment for extended periods of time, and become smarter autonomously. In this paper we show how a neural network model developed by neuroscientists can be used by an autonomous robot to learn by trial-and-error when considering rewards delivered at arbitrary times, as would be the case of developmental robots interacting with humans in the real world. © Springer-Verlag Berlin Heidelberg 2001.