A proposal of reinforcement learning system to use knowledge effectively
Yukinobu Hoshino, Katsuhiko Kamei · Society of Instrument and Control Engineers of Japan · 2003
The machine learning is proposed to learn techniques of specialists. 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 the rules acquired by FEERL.