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Volumn 1, Issue , 2006, Pages 530-535

Decision tree methods for finding reusable MDP homomorphisms

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX ENVIRONMENTS; HOMOMORPHISMS; STATE ABSTRACTION; TRANSITION PROBABILITIES;

EID: 33750683324     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (19)

References (18)
  • 4
    • 0038517214 scopus 로고    scopus 로고
    • Equivalence notions and model minimization in markov decision processes
    • Givan, R.; Dean, T.; and Greig, M. 2003. Equivalence notions and model minimization in markov decision processes. Artificial Intelligence 147(1-2): 163-223.
    • (2003) Artificial Intelligence , vol.147 , Issue.1-2 , pp. 163-223
    • Givan, R.1    Dean, T.2    Greig, M.3
  • 5
    • 0034272032 scopus 로고    scopus 로고
    • Bounded-parameter markov decision processes
    • Givan, R.; Leach, S. M.; and Dean, T. 2000. Bounded-parameter markov decision processes. Artificial Intelligence 122(1-2):71-109.
    • (2000) Artificial Intelligence , vol.122 , Issue.1-2 , pp. 71-109
    • Givan, R.1    Leach, S.M.2    Dean, T.3
  • 9
    • 84898927961 scopus 로고    scopus 로고
    • Automated state abstraction for options using the u-tree algorithm
    • Jonsson, A., and Barto, A. 2001. Automated state abstraction for options using the u-tree algorithm. In Advances in Neural Information Processing Systems 13, 1054-1060.
    • (2001) Advances in Neural Information Processing Systems , vol.13 , pp. 1054-1060
    • Jonsson, A.1    Barto, A.2
  • 16
    • 0033170372 scopus 로고    scopus 로고
    • Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
    • Sutton, R. S.; Precup, D.; and Singh, S. P. 1999. Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence 112:181-211.
    • (1999) Artificial Intelligence , vol.112 , pp. 181-211
    • Sutton, R.S.1    Precup, D.2    Singh, S.P.3
  • 17
    • 0002557085 scopus 로고
    • Learning to perceive and act by trial and error
    • Whitehead, S., and Ballard, D. 1991. Learning to perceive and act by trial and error. Machine Learning 7:45-83.
    • (1991) Machine Learning , vol.7 , pp. 45-83
    • Whitehead, S.1    Ballard, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.