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Volumn 227, Issue , 2007, Pages 737-744

Analyzing feature generation for value-function approximation

Author keywords

[No Author keywords available]

Indexed keywords

ACOUSTIC NOISE; APPROXIMATION THEORY; ERROR ANALYSIS; FUNCTION EVALUATION;

EID: 34547982545     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273589     Document Type: Conference Paper
Times cited : (102)

References (15)
  • 2
    • 85153940465 scopus 로고
    • Generalization in reinforcement learning: Safely approximating the value function
    • Cambridge, MA: The MIT Press
    • Boyan, J. A., & Moore, A. W. (1995). Generalization in reinforcement learning: Safely approximating the value function. Advances in Neural Information Processing Systems 7 (pp. 369-376). Cambridge, MA: The MIT Press.
    • (1995) Advances in Neural Information Processing Systems 7 , pp. 369-376
    • Boyan, J.A.1    Moore, A.W.2
  • 3
    • 0001771345 scopus 로고    scopus 로고
    • Linear least-squares algorithms for temporal difference learning
    • Bradtke, S., & Barto, A. (1996). Linear least-squares algorithms for temporal difference learning. Machine Learning, 2, 33-58.
    • (1996) Machine Learning , vol.2 , pp. 33-58
    • Bradtke, S.1    Barto, A.2
  • 4
    • 84983110889 scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Proc. of the Second European Conference on Computational Learning Theory
    • Freund, Y., & Schapire, R. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. Proc. of the Second European Conference on Computational Learning Theory. LNCS.
    • (1995) LNCS
    • Freund, Y.1    Schapire, R.2
  • 10
    • 17444414191 scopus 로고    scopus 로고
    • Basis function adaptation in temporal difference reinforcement learning
    • Menache, I., Mannor, S., & Shimkin, N. (2005). Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134.
    • (2005) Annals of Operations Research , pp. 134
    • Menache, I.1    Mannor, S.2    Shimkin, N.3
  • 12
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3, 9-44.
    • (1988) Machine Learning , vol.3 , pp. 9-44
    • Sutton, R.S.1
  • 14
    • 84887252594 scopus 로고    scopus 로고
    • Support vector method for function approximation, regression estimation, and signal processing
    • Cambridge, MA: MIT Press
    • Vapnik, V., Golowich, S., & Smola, A. (1997). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9 (pp. 281-287). Cambridge, MA: MIT Press.
    • (1997) Advances in Neural Information Processing Systems 9 , pp. 281-287
    • Vapnik, V.1    Golowich, S.2    Smola, A.3
  • 15
    • 34547991475 scopus 로고    scopus 로고
    • Convergence results for some temporal difference methods based on least squares
    • LIDS-2697, Laboratory for Information and Decision Systems, Massachusetts Institute of Technology
    • Yu, H., & Bertsekas, D. (2006). Convergence results for some temporal difference methods based on least squares (Technical Report LIDS-2697). Laboratory for Information and Decision Systems, Massachusetts Institute of Technology.
    • (2006) Technical Report
    • Yu, H.1    Bertsekas, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.