NDLI: Hierarchical reinforcement learning and decision making for intelligent machines

Content Provider	IEEE Xplore Digital Library
Author	Lima, P. Saridis, G.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. Comput. & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA (Lima, P.; Saridis, G.)
Abstract	A methodology for performance improvement of intelligent machines based on hierarchical reinforcement learning is introduced. Machine decision making and learning are based on a cost function which includes reliability and a computational cost of algorithms at the three levels of the hierarchy proposed by Saridis. Despite this particular formalization, the methodology intends to be sufficiently general to encompass different types of architectures and applications. Novel contributions of this work include the definition of a cost function combining reliability and complexity, recursively improved through feedback, a hierarchical reinforcement learning and decision making algorithm which uses that cost function, and a methodology supported on information-based complexity for joint measure of algorithm cost and reliability. Results of simulations show the application of the formalism to intelligent robotic systems.
Starting Page	33
Ending Page	38
File Size	600326
Page Count	6
File Format	PDF
ISBN	0818653302
DOI	10.1109/ROBOT.1994.351014
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-05-08
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision making Cost function Intelligent robots Computational intelligence Learning systems Machine learning Computational efficiency Computer architecture Feedback Intelligent systems
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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