Homeokinetic reinforcement learning

Authors:
Simón C. Smith;J. Michael Herrmann
Affiliations:
Institute of Perception, Action and Behaviour, School of Informatics, The University of Edinburgh, Edinburgh, U.K.;Institute of Perception, Action and Behaviour, School of Informatics, The University of Edinburgh, Edinburgh, U.K.
Venue:
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Year:
2011

Citing 4
Cited 0

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Abstract

In order to find a control policy for an autonomous robot by reinforcement learning, the utility of a behaviour can be revealed locally through a modulation of the motor command by probing actions. For robots with many degrees of freedom, this type of exploration becomes inefficient such that it is an interesting option to use an auxiliary controller for the selection of promising probing actions. We suggest here to optimise the exploratory modulation by a self-organising controller. The approach is illustrated by two control tasks, namely swing-up of a pendulum and walking in a simulated hexapod. The results imply that the homeokinetic approach is beneficial for high complexity problems.