DocumentCode :
1345247
Title :
A parallel fuzzy inference model with distributed prediction scheme for reinforcement learning
Author :
Kuo, Yau-Hwang ; Hsu, Jang-Pong ; Wang, Cheng-Wen
Author_Institution :
Inst. of Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume :
28
Issue :
2
fYear :
1998
fDate :
4/1/1998 12:00:00 AM
Firstpage :
160
Lastpage :
172
Abstract :
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
Keywords :
fuzzy logic; fuzzy neural nets; inference mechanisms; learning (artificial intelligence); RFNN-DPS; distributed prediction scheme; explicit representation; fuzzy rules; internal reinforcement; parallel fuzzy inference model; reinforcement fuzzy neural network; reinforcement learning; rule reliability; Fuzzy logic; Fuzzy neural networks; Fuzzy set theory; Fuzzy systems; Inference algorithms; Neural networks; Neurofeedback; Predictive models; Stochastic processes; Supervised learning;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/3477.662757
Filename :
662757
Link To Document :
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