篇名 | A Study on Multi-Dimensional Fuzzy Q-learning for Intelligent Robots |
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卷期 | 9:2 |
作者 | Kazuo Kiguchi 、 Hui He 、 Kenbu Teramoto |
頁次 | 095-104 |
關鍵字 | Intelligent robot 、 Fuzzy Q-learning 、 Multi-dimensional learning 、 Behavior selection 、 EI 、 SCI 、 SCIE 、 Scopus |
出刊日期 | 200706 |
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.