Name of Paper

Increased learning rates through the sharing of experiences of multiple autonomous mobile robot agents


Authors

I.D. Kelly and D.A. Keating


Published

1998 IEEE-FUZZ World Congress on Computational Intelligence, Anchorage, Alaska. May. pp 129-134.


Abstract

This paper describes a reinforcement learning algorithm for small autonomous mobile robot agents based on sets of fuzzy automata. The task of the robots is to learn how to reactively avoid obstacles. In the approach presented here two or four robots learn simultaneously, with the experiences of each robot being passed onto the other(s). It is shown that an increasing number of robots sharing their experiences results in a faster and more repeatable learning of each robot’s behavioural parameters.


Electronic copy

wcci98.ps.Z (400K)