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Volumn 5323 LNAI, Issue , 2008, Pages 96-109

Bayesian reward filtering

Author keywords

Bayesian Filtering; Function Approximation; Reinforcement Learning

Indexed keywords

KALMAN FILTERS; NEURAL NETWORKS;

EID: 58449117448     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89722-4_8     Document Type: Conference Paper
Times cited : (5)

References (13)
  • 2
    • 58449086942 scopus 로고    scopus 로고
    • Chen, Z.: Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond. Technical report, Adaptive Systems Lab, McMaster University (2003)
    • Chen, Z.: Bayesian Filtering: From Kalman Filters to Particle Filters, and Beyond. Technical report, Adaptive Systems Lab, McMaster University (2003)
  • 6
    • 0742306447 scopus 로고    scopus 로고
    • Szita, I., Lo″rincz, A.: Kalman Filter Control Embedded into the Reinforcement Learning Framework. Neural Comput. 16(3), 491-499 (2004)
    • Szita, I., Lo″rincz, A.: Kalman Filter Control Embedded into the Reinforcement Learning Framework. Neural Comput. 16(3), 491-499 (2004)
  • 7
    • 34547974097 scopus 로고    scopus 로고
    • Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation
    • Phua, C.W., Fitch, R.: Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation. In: ICML 2007 (2007)
    • (2007) ICML 2007
    • Phua, C.W.1    Fitch, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.