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Abstract: Fuzzy Actor-Critic Learning (FACL) and Fuzzy Q-Learning (FQL) are reinforcement learning methods based on Dynamic Programming (DP) principles. In this paper they are used to tune on line the conclusion part of Fuzzy Inference Systems (FIS). The only information available for learning is the system feedback, which describes in terms of reward and punishment the task the fuzzy agent has to realize. At each time step, the agent receives a reinforcement signal according to the last action it has... (Update)
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BibTeX entry: (Update)
L. Jouffe, Fuzzy Inference Systems Learning by Reinforcement Methods: Application to a Pig House Atmosphere Control, Ph.D. thesis, University of Rennes I, 1997, (in French). http://citeseer.ist.psu.edu/jouffe97fuzzy.html More
@misc{ jouffe97fuzzy,
author = "L. Jouffe",
title = "Fuzzy Inference Systems Learning by Reinforcement Methods: Application
to a Pig House Atmosphere Control",
text = "L. Jouffe, Fuzzy Inference Systems Learning by Reinforcement Methods: Application
to a Pig House Atmosphere Control, Ph.D. thesis, University of Rennes I,
1997, (in French).",
year = "1997",
url = "citeseer.ist.psu.edu/jouffe97fuzzy.html" }
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[Article contains additional citations not shown here]
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