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Volumn , Issue , 2008, Pages 476-483

Model-based Bayesian reinforcement learning in large structured domains

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN FRAMEWORKS; MODEL PARAMETERS; ON-LINE PLANNING; OPTIMAL SEQUENCE;

EID: 77955213275     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (42)

References (12)
  • 2
    • 0034248853 scopus 로고    scopus 로고
    • Stochastic dynamic programming with factored representations
    • Boutilier, C., Dearden, R., & Goldszmidt, M. (2000). Stochastic dynamic programming with factored representations. Artif. Intel., 121(1-2), 49-107.
    • (2000) Artif. Intel. , vol.121 , Issue.1-2 , pp. 49-107
    • Boutilier, C.1    Dearden, R.2    Goldszmidt, M.3
  • 3
    • 1142281527 scopus 로고    scopus 로고
    • Model based bayesian exploration
    • Dearden, R., Friedman, N., & Andre, D. (1999). Model based bayesian exploration. In UAI, pp. 150-159.
    • (1999) UAI , pp. 150-159
    • Dearden, R.1    Friedman, N.2    Andre, D.3
  • 5
    • 80053158041 scopus 로고    scopus 로고
    • Bayesian structure learning using dynamic programming and MCMC
    • Eaton, D., & Murphy, K. (2007). Bayesian structure learning using dynamic programming and MCMC. In UAI.
    • (2007) UAI
    • Eaton, D.1    Murphy, K.2
  • 6
    • 0037262841 scopus 로고    scopus 로고
    • Being Bayesian about Bayesian network structure: A Bayesian approach to structure discovery in Bayesian networks
    • Friedman, N., & Koller, D. (2003). Being Bayesian about Bayesian network structure: A Bayesian approach to structure discovery in Bayesian networks. Machine Learning, 50(1-2), 95-125.
    • (2003) Machine Learning , vol.50 , Issue.1-2 , pp. 95-125
    • Friedman, N.1    Koller, D.2
  • 8
    • 34249761849 scopus 로고
    • Learning bayesian networks: The combination of knowledge and statistical data
    • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197-243.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 197-243
    • Heckerman, D.1    Geiger, D.2    Chickering, D.M.3
  • 9
    • 0032073263 scopus 로고    scopus 로고
    • Planning and acting in partially observable stochastic domains
    • PII S000437029800023X
    • Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998). Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101(1-2), 99-134. (Pubitemid 128387390)
    • (1998) Artificial Intelligence , vol.101 , Issue.1-2 , pp. 99-134
    • Kaelbling, L.P.1    Littman, M.L.2    Cassandra, A.R.3
  • 10
    • 0004835198 scopus 로고    scopus 로고
    • Approximate planning for factored POMDPs using belief state simplification
    • McAllester, D., & Singh, S. (1999). Approximate Planning for Factored POMDPs using Belief State Simplification. In UAI, pp. 409-416.
    • (1999) UAI , pp. 409-416
    • McAllester, D.1    Singh, S.2
  • 11
    • 34250730267 scopus 로고    scopus 로고
    • An analytic solution to discrete Bayesian reinforcement learning
    • Poupart, P., Vlassis, N., Hoey, J., & Regan, K. (2006). An analytic solution to discrete Bayesian reinforcement learning. In ICML, pp. 697-704.
    • (2006) ICML , pp. 697-704
    • Poupart, P.1    Vlassis, N.2    Hoey, J.3    Regan, K.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.