A parallel fuzzy inference model with distributed prediction scheme for reinforcement learning

Yau Hwang Kuo, Jang Pong Hsu, Cheng Wen Wang

研究成果: Article同行評審

15 引文 斯高帕斯(Scopus)


This paper proposes a three-layered parallel fuzzy inference model called reinforcement fuzzy neural network with distributed prediction scheme (RFNN-DPS), which performs reinforcement learning with a novel distributed prediction scheme. In RFNN-DPS, an additional predictor for predicting the external reinforcement signal is not necessary, and the internal reinforcement information is distributed into fuzzy rules (rule nodes). Therefore, using RFNN-DPS, only one network is needed to construct a fuzzy logic system with the abilities of parallel inference and reinforcement learning. Basically, the information for prediction in RFNN-DPS is composed of credit values stored in fuzzy rule nodes, where each node holds a credit vector to represent the reliability of the corresponding fuzzy rule. The credit values are not only accessed for predicting external reinforcement signals, but also provide a more profitable internal reinforcement signal to each fuzzy rule itself. RFNN-DPS performs a credit-based exploratory algorithm to adjust its internal status according to the internal reinforcement signal. During learning, the RFNN-DPS network is constructed by a single-step or multistep reinforcement learning algorithm based on the ART concept. According to our experimental results, RFNN-DPS shows the advantages of simple network structure, fast learning speed, and explicit representation of rule reliability.

頁(從 - 到)160-172
期刊IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
出版狀態Published - 1998

All Science Journal Classification (ASJC) codes

  • 控制與系統工程
  • 軟體
  • 資訊系統
  • 人機介面
  • 電腦科學應用
  • 電氣與電子工程


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