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Volumn 1, Issue , 2012, Pages 97-104

Near-optimal BRL using optimistic local transitions

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL EXPLOSION; HIGH PROBABILITY; LOCAL TRANSITIONS; SAMPLE COMPLEXITY; TRANSITION FUNCTIONS; UNKNOWN ENVIRONMENTS;

EID: 84867138336     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (25)

References (15)
  • 3
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    • R-max - A general polynomial time algorithm for near-optimal reinforcement learning
    • Brafman, R.I. and Tennenholtz, M. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. JMLR, 3:213-231, 2003.
    • (2003) JMLR , vol.3 , pp. 213-231
    • Brafman, R.I.1    Tennenholtz, M.2
  • 5
    • 0012257655 scopus 로고    scopus 로고
    • Near-optimal reinforcement learning in polynomial time
    • Kearns, M. and Singh, S. Near-optimal reinforcement learning in polynomial time. In Machine Learning, pp. 260-268, 1998.
    • (1998) Machine Learning , pp. 260-268
    • Kearns, M.1    Singh, S.2
  • 6
    • 71149109483 scopus 로고    scopus 로고
    • Near-Bayesian exploration in polynomial time
    • Kolter, J. and Ng, A. Near-Bayesian exploration in polynomial time. In Proc. of ICML, 2009.
    • Proc. of ICML, 2009
    • Kolter, J.1    Ng, A.2
  • 9
    • 80053165997 scopus 로고    scopus 로고
    • Variance-based rewards for approximate Bayesian reinforcement learning
    • Sorg, J., Singh, S., and Lewis, R. Variance-based rewards for approximate Bayesian reinforcement learning. In Proc. of UAI, 2010.
    • Proc. of UAI, 2010
    • Sorg, J.1    Singh, S.2    Lewis, R.3
  • 10
    • 73549084301 scopus 로고    scopus 로고
    • Reinforcement learning in finite MDPs: PAC analysis
    • December
    • Strehl, A.L., Li, L., and Littman, M.L. Reinforcement learning in finite MDPs: PAC analysis. JMLR, 10: 2413-2444, December 2009.
    • (2009) JMLR , vol.10 , pp. 2413-2444
    • Strehl, A.L.1    Li, L.2    Littman, M.L.3
  • 11
    • 14344258433 scopus 로고    scopus 로고
    • A Bayesian framework for rein- Forcement learning
    • Strens, Malcolm J. A. A Bayesian framework for rein- forcement learning. In Proc. of ICML, 2000.
    • Proc. of ICML, 2000
    • Strens, M.J.A.1
  • 13
    • 77956520676 scopus 로고    scopus 로고
    • Model-based reinforcement learning with nearly tight exploration complexity bounds
    • Szita, Istvn and Szepesvri, Csaba. Model-based reinforcement learning with nearly tight exploration complexity bounds. In Proc. of ICML, 2010.
    • Proc. of ICML, 2010
    • Szita, I.1    Szepesvri, C.2
  • 15
    • 79958846996 scopus 로고    scopus 로고
    • Exploring compact reinforcement-learning representations with linear regression
    • Walsh, T.J., Szita, I., Diuk, C., and Littman, M.L. Exploring compact reinforcement-learning representations with linear regression. In Proc. of UAI, 2009.
    • Proc. of UAI, 2009
    • Walsh, T.J.1    Szita, I.2    Diuk, C.3    Littman, M.L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.