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Integration of Genetic Programming and Reinforcement Learning for Real Robots
Shotaro Kamio, Hideyuki Mitsuhashi, and Hitoshi Iba
Graduate School of Frontier Science,
The University of Tokyo,
7-3-1 Hongo, Bunkyo-ku,
Tokyo, 113-8656, Japan.
{kamio,mituhasi,iba}@miv.t.u-tokyo.ac.jp
Abstract. We propose an integrated technique of genetic programming (GP) and reinforcement learning (RL) that allows a real robot to execute real-time learning. Our technique does not need a precise simulator because learning is done with a real robot. Moreover, our technique makes it possible to learn optimal actions in real robots. We show the result of an experiment with a real robot AIBO and represents the result which proves proposed technique performs better than traditional Q-learning method.
LNCS 2723, p. 470 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003