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Volumn 1, Issue , 2010, Pages 612-617

Integrating sample-based planning and model-based reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL EFFICIENCY; PLANNING; REINFORCEMENT LEARNING;

EID: 77958578580     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (58)

References (17)
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    • Balla, R.-K.1    Fern, A.2
  • 2
    • 0034248853 scopus 로고    scopus 로고
    • Stochastic dynamic programming with factored representations
    • Boutilier, C; Dearden, R.; and Goldszmidt, M. 2000. Stochastic dynamic programming with factored representations. Artificial Intelligence 121(1):49-107.
    • (2000) Artificial Intelligence , vol.121 , Issue.1 , pp. 49-107
    • Boutilier, C.1    Dearden, R.2    Goldszmidt, M.3
  • 3
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    • Bandit algorithms for tree search
    • Coquelin, P.-A., and Munos, R. 2007. Bandit algorithms for tree search. In UAI.
    • (2007) UAI
    • Coquelin, P.-A.1    Munos, R.2
  • 4
    • 84880882489 scopus 로고    scopus 로고
    • Online learning and exploiting relational models in reinforcement learning
    • Croonenborghs, T.; Ramon, J.; Blocked, H.; and Bruynooghe, M. 2007. Online learning and exploiting relational models in reinforcement learning. In IJCAI.
    • (2007) IJCAI
    • Croonenborghs, T.1    Ramon, J.2    Blocked, H.3    Bruynooghe, M.4
  • 5
    • 33749242809 scopus 로고    scopus 로고
    • Learning the structure of factored Markov decision processes in reinforcement learning problems
    • Degris, T; Sigaud, O.; and Wuillemin, P.-H. 2006. Learning the structure of factored Markov decision processes in reinforcement learning problems. In ICML.
    • (2006) ICML
    • Degris, T.1    Sigaud, O.2    Wuillemin, P.-H.3
  • 6
    • 77958578450 scopus 로고    scopus 로고
    • Combining online and offline knowledge in UCT
    • Gelly, S., and Silver, D. 2007. Combining online and offline knowledge in UCT. In ICML.
    • (2007) ICML
    • Gelly, S.1    Silver, D.2
  • 7
    • 0036832951 scopus 로고    scopus 로고
    • A sparse sampling algorithm for near-optimal planning in large Markov decision processes
    • Kearns, M.; Mansour, Y.; and Ng, A. Y. 2002. A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Machine Learning 49:193-208.
    • (2002) Machine Learning , vol.49 , pp. 193-208
    • Kearns, M.1    Mansour, Y.2    Ng, A.Y.3
  • 8
    • 34547975806 scopus 로고    scopus 로고
    • Bandit based Monte-Carlo planning
    • Kocsis, L., and Szepesvari, C. 2006. Bandit based Monte-Carlo planning. In ECML.
    • (2006) ECML
    • Kocsis, L.1    Szepesvari, C.2
  • 9
    • 71149086468 scopus 로고    scopus 로고
    • Approximate inference for planning in stochastic relational worlds
    • Lang, T, and Toussaint, M. 2009. Approximate inference for planning in stochastic relational worlds. In ICML.
    • (2009) ICML
    • Lang, T.1    Toussaint, M.2
  • 10
    • 56449122733 scopus 로고    scopus 로고
    • Knows what it knows: A framework for self-aware learning
    • Li, L.; Littman, M. L.; and Walsh, T. J. 2008. Knows what it knows: A framework for self-aware learning. In ICML.
    • (2008) ICML
    • Li, L.1    Littman, M.L.2    Walsh, T.J.3
  • 14
    • 56449110907 scopus 로고    scopus 로고
    • Sample-based learning and search with permanent and transient memories
    • Silver, D.; Sutton, R. S.; and Müller, M. 2008. Sample-based learning and search with permanent and transient memories. In ICML.
    • (2008) ICML
    • Silver, D.1    Sutton, R.S.2    Müller, M.3
  • 17
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    • Exploring compact reinforcement-learning representations with linear regression
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    • Walsh, T.J.1    Szita, I.2    Diuk, C.3    Littman, M.L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.