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Volumn 1, Issue , 2007, Pages 645-650

Efficient structure learning in factored-state MDPs

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DATA STRUCTURES; LEARNING ALGORITHMS; PROBABILITY; PROBLEM SOLVING;

EID: 36348930987     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (84)

References (11)
  • 1
    • 33747670266 scopus 로고    scopus 로고
    • Learning factor graphs in polynomial time and sample complexity
    • Abbeel, P.; Koller, D.; and Ng, A. Y. 2006. Learning factor graphs in polynomial time and sample complexity. JMLR.
    • (2006) JMLR
    • Abbeel, P.1    Koller, D.2    Ng, A.Y.3
  • 2
    • 0346942368 scopus 로고    scopus 로고
    • Decision-theoretic planning: Structural assumptions and computational leverage
    • Boutilier, C.; Dean, T.; and Hanks, S. 1999. Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11:1-94.
    • (1999) Journal of Artificial Intelligence Research , vol.11 , pp. 1-94
    • Boutilier, C.1    Dean, T.2    Hanks, S.3
  • 3
    • 0041965975 scopus 로고    scopus 로고
    • R-MAX-a general polynomial time algorithm for near-optimal reinforcement learning
    • Brafman, R. I., and Tennenholtz, M. 2002. R-MAX-a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3:213-231.
    • (2002) Journal of Machine Learning Research , vol.3 , pp. 213-231
    • Brafman, R.I.1    Tennenholtz, M.2
  • 5
    • 0002278788 scopus 로고    scopus 로고
    • Hierarchical reinforcement learning with the MAXQ value function decomposition
    • Dietterich, T. G. 2000. Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13:227-303.
    • (2000) Journal of Artificial Intelligence Research , vol.13 , pp. 227-303
    • Dietterich, T.G.1
  • 7
    • 23244466805 scopus 로고    scopus 로고
    • Ph.D. Dissertation, Gatsby Computational Neuroscience Unit, University College London
    • Kakade, S. M. 2003. On the Sample Complexity of Reinforcement Learning. Ph.D. Dissertation, Gatsby Computational Neuroscience Unit, University College London.
    • (2003) On the Sample Complexity of Reinforcement Learning
    • Kakade, S.M.1
  • 9
    • 0036832954 scopus 로고    scopus 로고
    • Near-optimal reinforcement learning in polynomial time
    • Kearns, M. J., and Singh, S. P. 2002. Near-optimal reinforcement learning in polynomial time. Machine Learning 49(2-3):209-232.
    • (2002) Machine Learning , vol.49 , Issue.2-3 , pp. 209-232
    • Kearns, M.J.1    Singh, S.P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.