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Volumn , Issue , 1997, Pages 1005-1011

Multidimensional triangulation and interpolation for reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

INTERPOLATION; ITERATIVE METHODS; LEARNING ALGORITHMS; MARKOV PROCESSES; TRIANGULATION;

EID: 0345062531     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (48)

References (12)
  • 2
    • 0001133021 scopus 로고
    • Generalization in reinforcement learning: Safely approximating the value function
    • J. A. Boyan and A. W. Moore. Generalization in Reinforcement Learning: Safely Approximating the Value Function. In Neural Information Processing Systems 7, 1995.
    • (1995) Neural Information Processing Systems , vol.7
    • Boyan, J.A.1    Moore, A.W.2
  • 3
    • 0003259931 scopus 로고    scopus 로고
    • Improving elevator performance using reinforcement learning
    • D. Touretzky, M. Mozer, and M. Hasselmo, editors
    • R. H. Crites and A. G. Barto. Improving Elevator Performance using Reinforcement Learning. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Neural Information Processing Systems 8, 1996.
    • (1996) Neural Information Processing Systems , vol.8
    • Crites, R.H.1    Barto, A.G.2
  • 5
    • 0027684215 scopus 로고
    • Prioritized sweeping: Reinforcement learning with less data and less real time
    • A. W. Moore and C. G. Atkeson. Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time. Machine Learning, 13, 1993.
    • (1993) Machine Learning , vol.13
    • Moore, A.W.1    Atkeson, C.G.2
  • 6
    • 0029514510 scopus 로고
    • The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces
    • A. W. Moore and C. G. Atkeson. The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces. Machine Learning, 21, 1995.
    • (1995) Machine Learning , vol.21
    • Moore, A.W.1    Atkeson, C.G.2
  • 9
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • R. S. Sutton. Learning to Predict by the Methods of Temporal Differences. Machine Learning, 3:9-44, 1988.
    • (1988) Machine Learning , vol.3 , pp. 9-44
    • Sutton, R.S.1
  • 10
    • 0000723997 scopus 로고    scopus 로고
    • Generalization in reinforcement learning. Successful examples using sparse coarse coding
    • D. Touretzky, M. Mozer, and M. Hasselmo, editors
    • R. S. Sutton. Generalization in Reinforcement Learning. Successful Examples Using Sparse Coarse Coding. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Neural Information Processing Systems 8, 1996.
    • (1996) Neural Information Processing Systems , vol.8
    • Sutton, R.S.1
  • 11
    • 84899024442 scopus 로고
    • Practical issues in temporal difference learning
    • 76307 IBM t. 3. Watson Research Center, NY
    • G. J. Tesauro. Practical Issues in Temporal Difference Learning. RC 17223 (76307), IBM t. 3. Watson Research Center, NY, 1991.
    • (1991) RC 17223
    • Tesauro, G.J.1
  • 12
    • 0004049893 scopus 로고
    • PhD. Thesis, Kings College, University of Cambridge, May
    • C. J. C. H. Watkins. Learning from Delayed Rewards. PhD. Thesis, Kings College, University of Cambridge, May 1989.
    • (1989) Learning from Delayed Rewards
    • Watkins, C.J.C.H.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.