Self-scaling reinforcement learning for fuzzy logiccontroller-applications to motion control of two-link brachiation robot
Hasegawa, Y.
Fukuda, T.
Shimojima, K.
Dept. of Microeng., Nagoya Univ.;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Dec 1999
Volume: 46,
Issue: 6
On page(s): 1123-1131
ISSN: 0278-0046
References Cited: 19
CODEN: ITIED6
INSPEC Accession Number: 6453968
Digital Object Identifier: 10.1109/41.807999
Current Version Published: 2002-08-06
Abstract
In this paper, we propose a new reinforcement learning algorithm
to generate a fuzzy controller for robot motions. This algorithm
generates a range of continuous real-valued actions, and the
reinforcement signal is self-scaled. This prevents the weights from
overshooting when the system receives very large reinforcement values.
Therefore, this algorithm can obtain a solution in fewer iterations. The
proposed method is applied to the control of the brachiation robot,
which moves dynamically from branch to branch like a gibbon swinging its
body in a pendulum-like fashion. Through computer simulations, we show
the fast convergence and the robustness against disturbances
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