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Volumn 1, Issue , 2006, Pages 518-523

Sample-efficient evolutionary function approximation for reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY FUNCTION APPROXIMATION; FUNCTION APPROXIMATORS; LEARNING PROBLEMS; PARAMETERIZED FUNCTIONS;

EID: 33750687531     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (7)

References (22)
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    • Baldwin, J.M.1
  • 5
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    • Elevator group control using multiple reinforcement learning agents
    • Crites, R. H., and Barto, A. G. 1998. Elevator group control using multiple reinforcement learning agents. Machine Learning 33(2-3):235-262.
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    • Crites, R.H.1    Barto, A.G.2
  • 7
    • 0000211184 scopus 로고
    • How learning can guide evolution
    • Hinton, G.E., and Nowlan, S. J. 1987. How learning can guide evolution. Complex Systems 1:495-502.
    • (1987) Complex Systems , vol.1 , pp. 495-502
    • Hinton, G.E.1    Nowlan, S.J.2
  • 8
    • 0037253062 scopus 로고    scopus 로고
    • The vision of autonomie computing
    • Kephart, J. O., and Chess, D. M. 2003. The vision of autonomie computing. Computer 36(1):41-50.
    • (2003) Computer , vol.36 , Issue.1 , pp. 41-50
    • Kephart, J.O.1    Chess, D.M.2
  • 11
    • 0000123778 scopus 로고
    • Self-improving reactive agents based on reinforcement learning, planning, and teaching
    • Lin, L.-J. 1992. Self-improving reactive agents based on reinforcement learning, planning, and teaching. Machine Learning 8(3-4):293-321.
    • (1992) Machine Learning , vol.8 , Issue.3-4 , pp. 293-321
    • Lin, L.-J.1
  • 12
    • 0027684215 scopus 로고
    • Prioritized sweeping: Reinforcement learning with less data and less time
    • Moore, A. W., and Atkeson, C. G. 1993. Prioritized sweeping: Reinforcement learning with less data and less time. Machine Learning 13(1):103-130.
    • (1993) Machine Learning , vol.13 , Issue.1 , pp. 103-130
    • Moore, A.W.1    Atkeson, C.G.2
  • 13
    • 33646398129 scopus 로고    scopus 로고
    • Neural fitted Q iteration - First experiences with a data efficient neural reinforcement learning method
    • Reidmiller, M. 2005. Neural fitted Q iteration - first experiences with a data efficient neural reinforcement learning method. In Proceedings of the Sixteenth European Conference on Machine Learning, 317-328.
    • (2005) Proceedings of the Sixteenth European Conference on Machine Learning , pp. 317-328
    • Reidmiller, M.1
  • 15
    • 0036594106 scopus 로고    scopus 로고
    • Evolving neural networks through augmenting topologies
    • Stanley, K. O., and Miikkulainen, R. 2002. Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2):99-127.
    • (2002) Evolutionary Computation , vol.10 , Issue.2 , pp. 99-127
    • Stanley, K.O.1    Miikkulainen, R.2
  • 18
    • 0000985504 scopus 로고
    • TD-Gammon, a self-teaching backgammon program, achieves master-level play
    • Tesauro, G. 1994. TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation 6(2):215-219.
    • (1994) Neural Computation , vol.6 , Issue.2 , pp. 215-219
    • Tesauro, G.1
  • 21
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    • Evolutionary function approximation for reinforcement learning
    • To appear
    • Whiteson, S., and Stone, P. 2006. Evolutionary function approximation for reinforcement learning. Journal of Machine Learning Research. To appear.
    • (2006) Journal of Machine Learning Research
    • Whiteson, S.1    Stone, P.2
  • 22
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    • Evolving artificial neural networks
    • Yao, X. 1999. Evolving artificial neural networks. Proceedings of the IEEE 87(9):1423-1447.
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    • Yao, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.