A study on multi-dimensional fuzzy Q-learning for intelligent robots

Kazuo Kiguchi, He Hui, Kenbu Teramoto

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Reinforcement learning is one of the most important learning methods for intelligent robots working in unknown/uncertain environments. Multi-dimensional fuzzy Q-learning, an extension of the Q-learning method, has been proposed in this study. The proposed method has been applied for an intelligent robot working in a dynamic environment. The rewards from the evaluation functions and the fuzzy Q-values generated by the neural networks (fuzzy Q-net) are expressed in vector forms in order to obtain optimal behaviors for several different purposes. By applying this learning method, evaluation and learning of fuzzy Q-values for the other behaviors can be carried out simultaneously in one trial. We express fuzzy states as the vector of fuzzy sets for input variables of the fuzzy Q-net. The behavior selection algorithm is also proposed in this study. The simulation results show the effectives of the proposed methods for a mobile robot selects optimal behavior in a dynamic environment.

Original languageEnglish
Pages (from-to)95-104
Number of pages10
JournalInternational Journal of Fuzzy Systems
Issue number2
Publication statusPublished - Jun 2007
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Theoretical Computer Science
  • Computational Theory and Mathematics


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