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On the integration of reinforcement learning and approximate reasoning for controlThe author discusses the importance of strengthening the knowledge representation characteristic of reinforcement learning techniques using methods such as approximate reasoning. The ARIC (approximate reasoning-based intelligent control) architecture is an example of such a hybrid approach in which the fuzzy control rules are modified (fine-tuned) using reinforcement learning. ARIC also demonstrates that it is possible to start with an approximately correct control knowledge base and learn to refine this knowledge through further experience. On the other hand, techniques such as the TD (temporal difference) algorithm and Q-learning establish stronger theoretical foundations for their use in adaptive control and also in stability analysis of hybrid reinforcement learning and approximate reasoning-based controllers.
Document ID
19930029110
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Berenji, Hamid R.
(Sterling Federal Systems, Inc.; NASA, Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 15, 2013
Publication Date
January 1, 1991
Publication Information
Publication: In: IEEE Conference on Decision and Control, 30th, Brighton, United Kingdom, Dec. 11-13, 1991, Proceedings. Vol. 2 (A93-13001 02-63)
Publisher: Institute of Electrical and Electronics Engineers, Inc.
Subject Category
Cybernetics
Accession Number
93A13107
Distribution Limits
Public
Copyright
Other

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