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Collaborating Fuzzy Reinforcement Learning AgentsEarlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.
Document ID
20020085181
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Berenji, Hamid R.
(Intelligent Inference Systems Corp. Moffett Field, CA United States)
Date Acquired
August 20, 2013
Publication Date
January 1, 1997
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Meeting Information
Meeting: Seventh IFSA 1997 World Congress
Location: Prague
Country: Czechoslovakia
Start Date: June 25, 1997
End Date: June 29, 1997
Sponsors: International Fuzzy Systems Association
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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