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Volumn , Issue , 2008, Pages 41-47

Learning all optimal policies with multiple criteria

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE METHODS; MACHINE LEARNING; REINFORCEMENT LEARNING; ALGORITHMS; EDUCATION; LEARNING SYSTEMS; OPTIMIZATION; REINFORCEMENT; ROBOT LEARNING;

EID: 56449120027     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390162     Document Type: Conference Paper
Times cited : (191)

References (12)
  • 1
    • 56449115075 scopus 로고    scopus 로고
    • Abeel, P., & Ng, A. (2004). Apprentice learning via inverse reinforcement learning. Proc. ICML-04.
    • Abeel, P., & Ng, A. (2004). Apprentice learning via inverse reinforcement learning. Proc. ICML-04.
  • 2
    • 0004245883 scopus 로고    scopus 로고
    • Cambridge, Massachusetts: Cambridge University Press
    • Ainslie, G. (2001). Breakdown of will. Cambridge, Massachusetts: Cambridge University Press.
    • (2001) Breakdown of will
    • Ainslie, G.1
  • 3
    • 85012688561 scopus 로고
    • Princeton: Princeton University Press
    • Bellman, R. E. (1957). Dynamic programming. Princeton: Princeton University Press.
    • (1957) Dynamic programming
    • Bellman, R.E.1
  • 4
    • 0001702902 scopus 로고
    • Applications of random sampling in computational geometry, II
    • Clarkson, K. L., & Shor, P. W. (1989). Applications of random sampling in computational geometry, II. Discrete and Computational Geometry, 4, 387-421.
    • (1989) Discrete and Computational Geometry , vol.4 , pp. 387-421
    • Clarkson, K.L.1    Shor, P.W.2
  • 5
    • 0000184142 scopus 로고
    • Constrained markov decision models with weighted discounted rewards
    • Feinberg, E., & Schwartz, A. (1995). Constrained markov decision models with weighted discounted rewards. Mathematics of Operations Research, 20, 302-320.
    • (1995) Mathematics of Operations Research , vol.20 , pp. 302-320
    • Feinberg, E.1    Schwartz, A.2
  • 6
    • 56449099654 scopus 로고    scopus 로고
    • Gabor, Z., Kalmar, Z., & Szepesvari, C. (1998). Multi-criteria reinforcement learning. Proc. ICML-98.
    • Gabor, Z., Kalmar, Z., & Szepesvari, C. (1998). Multi-criteria reinforcement learning. Proc. ICML-98.
  • 8
    • 79960013704 scopus 로고    scopus 로고
    • A geometric approach to multi-criterion reinforcement learning
    • Marmor, S., & Shimkin, N. (2004). A geometric approach to multi-criterion reinforcement learning. Journal of Machine Learning Research, 325-360.
    • (2004) Journal of Machine Learning Research , pp. 325-360
    • Marmor, S.1    Shimkin, N.2
  • 9
    • 56449086645 scopus 로고    scopus 로고
    • Natarajan, S., & Tadepalli, P. (2005). Dynamic preferences in mult-criteria reinforcement learning. Proc. ICML-05. Bonn, Germany.
    • Natarajan, S., & Tadepalli, P. (2005). Dynamic preferences in mult-criteria reinforcement learning. Proc. ICML-05. Bonn, Germany.
  • 10
    • 56449090988 scopus 로고    scopus 로고
    • Ng, A., & Russell, S. (2000). Algorithms for inverse reinforcement learning. Proc. ICML-00.
    • Ng, A., & Russell, S. (2000). Algorithms for inverse reinforcement learning. Proc. ICML-00.
  • 11
    • 1942484759 scopus 로고    scopus 로고
    • Q-decomposition for reinforcement learning agents
    • Washington, DC
    • Russell, S., & Zimdars, A. (2003). Q-decomposition for reinforcement learning agents. Proc. ICML-03. Washington, DC.
    • (2003) Proc. ICML-03
    • Russell, S.1    Zimdars, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.