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Volumn 57, Issue 11, 2009, Pages 1119-1128

Automatic abstraction in reinforcement learning using data mining techniques

Author keywords

Abstraction; Clustering; Data mining; Reinforcement learning; Sequence mining

Indexed keywords

ABSTRACTION; ACTION SEQUENCES; CLUSTERING; DATA MINING ALGORITHM; DATA MINING TECHNIQUES; DATA SETS; LARGE DATASETS; LEARNING AGENTS; Q-LEARNING ALGORITHMS; SEQUENCE MINING; STATE CLUSTERING; STATE SPACE; STATE TRANSITIONS; TEMPORAL ABSTRACTION;

EID: 70350053106     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2009.07.002     Document Type: Article
Times cited : (25)

References (23)
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    • Sutton, R.1    Precup, D.2    Singh, S.3
  • 9
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    • Using relative novelty to identify useful temporal abstractions in reinforcement learning
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  • 18
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  • 21
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    • Bart Goethals, Survey on frequent pattern mining, HIIT Basic Research unit, University of Helsinki, Finland
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    • Park, K.H.1    Kim, Y.J.2    Kim, J.H.3
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    • Reinforcement learning for problems with symmetrical restricted states
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    • Kamal, M.A.S.1    Murata, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.