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Volumn , Issue , 2008, Pages 75-82

Basis function construction in reinforcement learning using cascade-correlation learning architecture

Author keywords

[No Author keywords available]

Indexed keywords

BASIS FUNCTIONS; BENCH-MARK PROBLEMS; INPUT DATUM; LEARNING ARCHITECTURES; LEAST SQUARES POLICY ITERATIONS; NEW APPROACHES; PROBLEM COMPLEXITY; VALUE FUNCTIONS;

EID: 60649120535     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2008.24     Document Type: Conference Paper
Times cited : (4)

References (15)
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    • S. E. Fahlman and C. Lebiere. The cascade-correlation learning architecture. In D. S. Touretzky, editor, Advances in NIPS, volume 2, pages 524-532. Morgan Kaufmann, 1990.
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    • Fahlman, S.E.1    Lebiere, C.2
  • 4
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    • Feature discovery in reinforcement learning using genetic programming
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    • S. Girgin and P. Preux. Feature discovery in reinforcement learning using genetic programming. In Proc. of Euro-GP, pages 218-229. Springer-Verlag, Mar. 2008.
    • (2008) Proc. of Euro-GP , pp. 218-229
    • Girgin, S.1    Preux, P.2
  • 5
    • 34547971381 scopus 로고    scopus 로고
    • Constructing basis functions from directed graphs for value function approximation
    • NY, USA, ACM
    • J. Johns and S. Mahadevan. Constructing basis functions from directed graphs for value function approximation. In ICML, pages 385-392, NY, USA, 2007. ACM.
    • (2007) ICML , pp. 385-392
    • Johns, J.1    Mahadevan, S.2
  • 6
    • 33749263205 scopus 로고    scopus 로고
    • Automatic basis function construction for approximate dynamic programming and reinforcement learning
    • NY, USA, ACM
    • P. W. Keller, S. Mannor, and D. Precup. Automatic basis function construction for approximate dynamic programming and reinforcement learning. In ICML, pages 449-456, NY, USA, 2006. ACM.
    • (2006) ICML , pp. 449-456
    • Keller, P.W.1    Mannor, S.2    Precup, D.3
  • 9
    • 35748957806 scopus 로고    scopus 로고
    • Proto-value functions: A laplacian framework for learning representation and control in markov decision processes
    • S. Mahadevan and M. Maggioni. Proto-value functions: A laplacian framework for learning representation and control in markov decision processes. J. of Machine Learning Research, 8:2169-2231, 2007.
    • (2007) J. of Machine Learning Research , vol.8 , pp. 2169-2231
    • Mahadevan, S.1    Maggioni, M.2
  • 10
    • 17444414191 scopus 로고    scopus 로고
    • Basis function adaptation in temporal difference reinforcement learning
    • I. Menache, S. Mannor, and N. Shimkin. Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134:215-238 (24), 2005.
    • (2005) Annals of Operations Research , vol.134 , Issue.24 , pp. 215-238
    • Menache, I.1    Mannor, S.2    Shimkin, N.3
  • 11
    • 34547982545 scopus 로고    scopus 로고
    • Analyzing feature generation for value-function approximation
    • NY, USA, ACM
    • R. Parr, C. Painter-Wakefield, L. Li, and M. Littman. Analyzing feature generation for value-function approximation. In ICML, pages 737-744, NY, USA, 2007. ACM.
    • (2007) ICML , pp. 737-744
    • Parr, R.1    Painter-Wakefield, C.2    Li, L.3    Littman, M.4
  • 12
    • 0003998452 scopus 로고
    • Markov Decision Processes - Discrete Stochastic Dynamic Programming
    • Wiley
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    • Puterman, M.1
  • 14
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    • Combining td-learning with cascade-correlation networks
    • T. Fawcett and N. Mishra, editors, AAAI Press
    • F. Rivest and D. Precup. Combining td-learning with cascade-correlation networks. In T. Fawcett and N. Mishra, editors, ICML, pages 632-639. AAAI Press, 2003.
    • (2003) ICML , pp. 632-639
    • Rivest, F.1    Precup, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.