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A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

contributor authorJianjun Ni
contributor authorMinghua Liu
contributor authorLi Ren
contributor authorYang, Simon X.
date accessioned2020-03-12T18:21:17Z
date available2020-03-12T18:21:17Z
date issued2014
identifier issn1545-5955
identifier other6410370.pdf
identifier urihttps://libsearch.um.ac.ir:443/fum/handle/fum/955687?locale-attribute=en&show=full
formatgeneral
languageEnglish
publisherIEEE
titleA Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System
typeJournal Paper
contenttypeMetadata Only
identifier padid7987424
subject keywordsenvironmental science computing
subject keywordslearning (artificial intelligence)
subject keywordsmulti-agent systems
subject keywordsresource allocation
subject keywordswater resources
subject keywordsadaptive reward value function
subject keywordsagent-based model
subject keywordsmaximum mapping value function-based Q-learning algorithm
subject keywordsmultiagent Q-learning-based optimal allocation approach
subject keywordsstakeholder agents
subject keywordsurban water resource management system
subject keywordsurban water resource optimal allocation
subject keywordswater environment system
subject keywordsBiological cells
subject keywordsGenetic algorithms
subject keywordsIndexes
subject keywordsOptimization
subject keywordsResource manageme
identifier doi10.1109/TASE.2012.2229978
journal titleAutomation Science and Engineering, IEEE Transactions on
journal volume11
journal issue1
filesize1474837
citations1


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