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Teaching robots to plan through Q-learning

Published online by Cambridge University Press:  11 March 2005

Dongbing Gu  and
Huosheng Hu
Dongbing Gu
Affiliation:
Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ (UK) E-mail: dgu@essex.ac.uk, hhu@essex.ac.uk
Huosheng Hu
Affiliation:
Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO4 3SQ (UK) E-mail: dgu@essex.ac.uk, hhu@essex.ac.uk
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Abstract

This paper presents a Q-learning approach to state-based planning of behaviour-based walking robots. The learning process consists of a teaching stage and an autonomous learning stage. During the teaching stage, the robot is instructed to operate in some interesting areas of the solution space to accumulate some prior knowledge. Then, the learning is switched to the autonomous learning stage to let the robot explore the solution space based on its prior knowledge. Experiments are conducted in the RoboCup domain and results show a good performance of the proposed method.

Keywords

Robot learningReinforcement learningBehaviour co-ordinationState-based planning
Type
Research Article
Information
Robotica , Volume 23 , Issue 2 , March 2005 , pp. 139 - 147
Copyright
© 2005 Cambridge University Press

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