Behaviour selection on a mobile robot using W-learning

Authors:
A. O. Martin Hallerdal;John C. T. Hallam
Affiliations:
Division of Informatics, University of Edinburgh, 5 Forrest Hill, Edinburgh EH1 2QL, United Kingdom;The Maersk Mc-Kinney Moller Institute for Production Technology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark
Venue:
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Year:
2002

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Abstract

A common approach in complex reinforcement learning tasks is to divide the problem into functional parts, or behaviours, and then to assign a sub-agent to solve each task. The action selection problem then becomes to negotiate between sub-agents with conflicting desires. W-learning is a method whereby agents build up W-values in each state that indicate how important that state is for that agent. These values are then used as basis for selecting agents. In this paper we present the first results, as far as we know, of applying W-learning on a mobile robot in solving a task in the real world. Results from the experiments are presented and the suitability of W-learning for real world robot tasks is discussed.