Effective Use of Learning Knowledge by FEERL

Yukinobu Hoshino and Katsuari Kamei

Computer Science, Ritsumeikan University, 1-1-1, Noji-Higashi, Kusatsu, Shiga 525-8577, Japan

Received:

August 28, 2002

Accepted:

October 21, 2002

Published:

February 20, 2003

Keywords:

knowledge, fuzzy resemblance reasoning, reinforcement learning, effective use

Abstract

The machine learning is proposed to learning techniques of spcialists. A machine has to learn techniques by trial and error when there are no training examples. Reinforcement learning is a powerful machine learning system, which is able to learn without giving training examples to a learning unit. But it is impossible for the reinforcement learning to support large environments because the number of if-then rules is a huge combination of a relationship between one environment and one action. We have proposed new reinforcement learning system for the large environment, Fuzzy Environment Evaluation Reinforcement Learning (FEERL). In this paper, we proposed to reuse of the acquired rules by FEERL.

Cite this article as:

Y. Hoshino and K. Kamei, “Effective Use of Learning Knowledge by FEERL,” J. Adv. Comput. Intell. Intell. Inform., Vol.7 No.1, pp. 6-9, 2003.

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