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Ito, Kazuyuki Okayama University
Imoto, Yoshiaki Okayama University
Taguchi, Hideaki Okayama University
Gofuku, Akio Okayama University
In this paper, we consider multi-agent system in which every agents have own tasks that differs each other. We propose a method that decreases learning time of reinforcement learning by using the model of environment. In the proposed algorithm, the model is created by sharing the experiences of agents each other. To demonstrate the effectiveness of the proposed method, simulations of a puddle world and experiments of a maze world have been carried out. As a result effective behaviors have been obtained quickly.
Published with permission from the copyright holder. This is the institute's copy, as published in Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on, 22-26 Aug. 2004, Pages 175-180.
Copyright © 2004 IEEE. All rights reserved.
Robotics and Biomimetics