A study of reinforcement learning with knowledge sharing - Applications to real mobile robots

Kazuyuki Ito, Yoshiaki Imoto, Hideaki Taguchi, Akio Gofuku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004
Pages175-180
Number of pages6
Publication statusPublished - Dec 1 2004
EventProceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004 - Shenyang, China
Duration: Aug 22 2004Aug 26 2004

Publication series

NameProceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004

Other

OtherProceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004
CountryChina
CityShenyang
Period8/22/048/26/04

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Ito, K., Imoto, Y., Taguchi, H., & Gofuku, A. (2004). A study of reinforcement learning with knowledge sharing - Applications to real mobile robots. In Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004 (pp. 175-180). (Proceedings - 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004).