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Volumn , Issue , 2009, Pages 1175-1180

Autonomously learning an action hierarchy using a learned qualitative state representation

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; MARKOV PROCESSES;

EID: 78751697580     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (23)

References (17)
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  • 2
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    • Exploiting structure in policy construction
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  • 3
    • 33749242809 scopus 로고    scopus 로고
    • Learning the structure of factored Markov decision processes in reinforcement learning problems
    • T. Degris, O. Sigaud, and P.H. Wuillemin. Learning the structure of factored Markov decision processes in reinforcement learning problems. In ICML, pages 257-264, 2006.
    • (2006) ICML , pp. 257-264
    • Degris, T.1    Sigaud, O.2    Wuillemin, P.H.3
  • 4
    • 0001806701 scopus 로고    scopus 로고
    • The MAXQ method for hierarchical reinforcement learning
    • T.G. Dietterich. The MAXQ method for hierarchical reinforcement learning. ICML, 1998.
    • (1998) ICML
    • Dietterich, T.G.1
  • 7
    • 14844364287 scopus 로고    scopus 로고
    • Breve: A 3d environment for the simulation of decentralized systems and artificial life
    • Jon Klein. Breve: a 3d environment for the simulation of decentralized systems and artificial life. In Proc. of the Int. Conf. on Artificial Life, 2003.
    • Proc. of the Int. Conf. on Artificial Life, 2003
    • Klein, J.1
  • 8
    • 0003474284 scopus 로고
    • The MIT Press, Cambridge, Massachusetts
    • Benjamin Kuipers. Qualitative Reasoning. The MIT Press, Cambridge, Massachusetts, 1994.
    • (1994) Qualitative Reasoning
    • Kuipers, B.1
  • 9
    • 0013465187 scopus 로고    scopus 로고
    • Automatic discovery of subgoals in reinforcement learning using diverse density
    • Amy McGovern and Andrew G. Barto. Automatic discovery of subgoals in reinforcement learning using diverse density. In ICML, pages 361-368, 2001.
    • (2001) ICML , pp. 361-368
    • McGovern, A.1    Barto, A.G.2
  • 10
    • 50849114173 scopus 로고    scopus 로고
    • Learning to predict the effects of actions: Synergy between rules and landmarks
    • J. Mugan and B. Kuipers. Learning to predict the effects of actions: Synergy between rules and landmarks. In ICDL, 2007.
    • (2007) ICDL
    • Mugan, J.1    Kuipers, B.2
  • 12
    • 34748875246 scopus 로고    scopus 로고
    • Learning symbolic models of stochastic domains
    • H.M. Pasula, L.S. Zettlemoyer, and L.P. Kaelbling. Learning symbolic models of stochastic domains. JAIR, 29:309-352, 2007.
    • (2007) JAIR , vol.29 , pp. 309-352
    • Pasula, H.M.1    Zettlemoyer, L.S.2    Kaelbling, L.P.3
  • 13
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    • J. Provost. sourceforge.net, 2008
    • J. Provost. sourceforge.net, 2008.
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    • Identifying useful subgoals in reinforcement learning by local graph partitioning
    • O. Simsek, A. Wolfe, and A. Barto. Identifying useful subgoals in reinforcement learning by local graph partitioning. ICML, pages 816-823, 2005.
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    • Simsek, O.1    Wolfe, A.2    Barto, A.3
  • 15
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    • Efficient structure learning in factored-state MDPs
    • A.L. Strehl, C. Diuk, and M.L. Littman. Efficient structure learning in factored-state MDPs. In AAAI, volume 22, page 645, 2007.
    • (2007) AAAI , vol.22 , pp. 645
    • Strehl, A.L.1    Diuk, C.2    Littman, M.L.3
  • 17
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    • Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
    • R. S. Sutton, D. Precup, and S. Singh. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1-2):181-211, 1999.
    • (1999) Artificial Intelligence , vol.112 , Issue.1-2 , pp. 181-211
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.