Document Detail


An information-theoretic analysis of return maximization in reinforcement learning.
MedLine Citation:
PMID:  21665429     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
We present a general analysis of return maximization in reinforcement learning. This analysis does not require assumptions of Markovianity, stationarity, and ergodicity for the stochastic sequential decision processes of reinforcement learning. Instead, our analysis assumes the asymptotic equipartition property fundamental to information theory, providing a substantially different view from that in the literature. As our main results, we show that return maximization is achieved by the overlap of typical and best sequence sets, and we present a class of stochastic sequential decision processes with the necessary condition for return maximization. We also describe several examples of best sequences in terms of return maximization in the class of stochastic sequential decision processes, which satisfy the necessary condition.
Authors:
Kazunori Iwata
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-5-17
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  -     ISSN:  1879-2782     ISO Abbreviation:  -     Publication Date:  2011 May 
Date Detail:
Created Date:  2011-6-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2011 Elsevier Ltd. All rights reserved.
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