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Volumn 2533, Issue , 2002, Pages 403-413

Feedforward neural networks in reinforcement learning applied to high-dimensional motor control

Author keywords

[No Author keywords available]

Indexed keywords

BACKPROPAGATION ALGORITHMS; GRADIENT METHODS; MACHINE LEARNING; REINFORCEMENT LEARNING;

EID: 84942750244     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36169-3_32     Document Type: Conference Paper
Times cited : (11)

References (22)
  • 1
    • 0344154963 scopus 로고
    • Strategy learning with multilayer connectionist representations
    • Irvine, CA, Morgan Kaufmann
    • Charles W. Anderson. Strategy learning with multilayer connectionist representations. In Proceedings of the Fourth International Workshop on Machine Learning, pages 103–114, Irvine, CA, 1987. Morgan Kaufmann.
    • (1987) Proceedings of the Fourth International Workshop on Machine Learning , pp. 103-114
    • Anderson, C.W.1
  • 2
    • 0027599793 scopus 로고
    • Universal approximation bounds for superpositions of a sigmoidal function
    • Maym
    • Andrew R. Barron. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, 39(3):930–945, Maym 1993.
    • (1993) IEEE Transactions on Information Theory , vol.39 , Issue.3 , pp. 930-945
    • Barron, A.R.1
  • 3
    • 85012688561 scopus 로고
    • Princeton University Press, Princeton, New Jersey
    • Richard Bellman. Dynamic Programming. Princeton University Press, Princeton, New Jersey, 1957.
    • (1957) Dynamic Programming
    • Bellman, R.1
  • 6
    • 0033629916 scopus 로고    scopus 로고
    • Reinforcement learning in continuous time and space
    • Kenji Doya. Reinforcement learning in continuous time and space. Neural Computation, 12:243–269, 2000.
    • (2000) Neural Computation , vol.12 , pp. 243-269
    • Doya, K.1
  • 10
    • 0027205884 scopus 로고
    • Møller. A scaled conjugate gradient algorithm for fast supervised learning
    • Martin F. Møller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 6:525–533, 1993.
    • (1993) Neural Networks , vol.6 , pp. 525-533
    • Martin, F.1
  • 12
    • 0009589301 scopus 로고    scopus 로고
    • How to train neural networks
    • Genevieve B. Orr and Klaus-Robert M¨uller, editors, Springer
    • Ralph Neuneier and Hans-Georg Zimmermann. How to train neural networks. In Genevieve B. Orr and Klaus-Robert M¨uller, editors, Neural Networks: Tricks of the Trade. Springer, 1998.
    • (1998) Neural Networks: Tricks of the Trade
    • Neuneier, R.1    Zimmermann, H.-G.2
  • 15
    • 0028374275 scopus 로고
    • Atkeson. Robot juggling: An implementation of memory-based learning
    • Stefan Schaal and Christopher G. Atkeson. Robot juggling: An implementation of memory-based learning. Control Systems Magazine, 14:57–71, 1994.
    • (1994) Control Systems Magazine , vol.14 , pp. 57-71
    • Schaal, S.1    Christopher, G.2
  • 18
    • 33847202724 scopus 로고
    • Sutton. Learning to predict by the methods of temporal differences
    • Richard 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
    • Richard, S.1
  • 19
    • 85156221438 scopus 로고    scopus 로고
    • Generalization in reinforcement learning: Successful examples using sparse coarse coding
    • MIT Press
    • Richard S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, pages 1038–1044. MIT Press, 1996.
    • (1996) Advances in Neural Information Processing Systems , pp. 1038-1044
    • Sutton, R.S.1
  • 21
    • 0029276036 scopus 로고
    • Temporal difference learning and TD-Gammon
    • MIT Press
    • Gerald Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3):58-68, March 1995.
    • (1995) Communications of the ACM , vol.38 , Issue.3 , pp. 58-68
    • Tesauro, G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.