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Kuremoto Takashi

Affiliate Master Yamaguchi University

A self-organized fuzzy-neuro reinforcement learning system for continuous state space for autonomous robots

Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation Volume 2008 Page 551-556
published_at 2008-12
2010010465.pdf
[fulltext] 1.05 MB
Title
A self-organized fuzzy-neuro reinforcement learning system for continuous state space for autonomous robots
Creators Obayashi Masanao
Creators Kuremoto Takashi
Creators Kobayashi Kunikazu
International Conference on Computational Intelligence for Modelling, Control and Automation : CIMCA 2008, Vienna, Austria, December 10-12, 2008.
This paper proposes the system that combines self-organized fuzzy-neural networks with reinforcement learning system (Q-learning, stochastic gradient ascent : SGA) to realize the autonomous robot behavior learning for continuous state space. The self-organized fuzzy neural network works as adaptive input state space classifier to adapt the change of environment, the part of reinforcement learning has the learning ability corresponding to rule for the input state space . Simultaneously, to simulate the real environment the robot has ability to estimate own-position. Finally, it is clarified that our proposed system is effective through the autonomous robot behavior learning simulation by using the khepera robot simulator.
Languages eng
Resource Type conference paper
Publishers Institute of Electrical and Electronics Engineers
Date Issued 2008-12
Rights
Copyright c2008 IEEE. Reprinted from Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation, 2008, p. 551-556. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Yamaguchi University Library's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.()
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info:doi/10.1109/CIMCA.2008.25
[ISBN]9780769535142
[isVersionOf] [URI]http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5172579
Schools 大学院理工学研究科(工学)