메뉴 건너뛰기




Volumn , Issue , 2017, Pages 1640-1646

Knowledge transfer for deep reinforcement learning with hierarchical experience replay

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DEEP LEARNING; DISTILLATION; KNOWLEDGE MANAGEMENT;

EID: 85030470931     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (112)

References (25)
  • 1
    • 84937961091 scopus 로고    scopus 로고
    • Do deep nets really need to be deep?
    • Ba, J., and Caruana, R. 2014. Do deep nets really need to be deep? In NIPS, 2654-2662.
    • (2014) NIPS , pp. 2654-2662
    • Ba, J.1    Caruana, R.2
  • 6
    • 84979924150 scopus 로고    scopus 로고
    • End-to-end training of deep visuomotor policies
    • Levine, S.; Finn, C.; Darrell, T.; and Abbeel, P. 2016. End-to-end training of deep visuomotor policies. JMLR 17(39):1-40.
    • (2016) JMLR , vol.17 , Issue.39 , pp. 1-40
    • Levine, S.1    Finn, C.2    Darrell, T.3    Abbeel, P.4
  • 7
    • 84910035297 scopus 로고    scopus 로고
    • Learning small-size dnn with output-distribution-based criteria
    • Li, J.; Zhao, R.; Huang, J.-T.; and Gong, Y. 2014. Learning small-size dnn with output-distribution-based criteria. In Interspeech, 1910-1914.
    • (2014) Interspeech , pp. 1910-1914
    • Li, J.1    Zhao, R.2    Huang, J.-T.3    Gong, Y.4
  • 11
    • 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
    • 85083953433 scopus 로고    scopus 로고
    • Actormimic deep multitask and transfer reinforcement learning
    • Parisotto, E.; Ba, J.; and Salakhutdinov, R. 2016. Actormimic deep multitask and transfer reinforcement learning. In ICLR.
    • (2016) ICLR
    • Parisotto, E.1    Ba, J.2    Salakhutdinov, R.3
  • 14
    • 21844480297 scopus 로고    scopus 로고
    • Generalized prioritized sweeping
    • Parr, D. A. N. F. R. 1998. Generalized prioritized sweeping. In NIPS.
    • (1998) NIPS
    • Parr, D.A.N.F.R.1
  • 19
    • 85062336054 scopus 로고    scopus 로고
    • Planning by prioritized sweeping with small backups
    • Seijen, H. V., and Sutton, R. S. 2013. Planning by prioritized sweeping with small backups. In ICML, 361-369.
    • (2013) ICML , pp. 361-369
    • Seijen, H.V.1    Sutton, R.S.2
  • 22
    • 84893343292 scopus 로고    scopus 로고
    • Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
    • Tieleman, T., and Hinton, G. 2012. Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning.
    • (2012) COURSERA: Neural Networks for Machine Learning
    • Tieleman, T.1    Hinton, G.2
  • 23
    • 85007210890 scopus 로고    scopus 로고
    • Deep reinforcement learning with double q-learning
    • Van Hasselt, H.; Guez, A.; and Silver, D. 2016. Deep reinforcement learning with double q-learning. In AAAI, 2094-2100.
    • (2016) AAAI , pp. 2094-2100
    • Van Hasselt, H.1    Guez, A.2    Silver, D.3
  • 24
    • 84998679057 scopus 로고    scopus 로고
    • Graying the black box: Understanding dqns
    • Zahavy, T.; Zrihem, N. B.; and Mannor, S. 2016. Graying the black box: Understanding dqns. In ICML, 1899-1908.
    • (2016) ICML , pp. 1899-1908
    • Zahavy, T.1    Zrihem, N.B.2    Mannor, S.3
  • 25
    • 84977555800 scopus 로고    scopus 로고
    • Learning deep neural network policies with continuous memory states
    • IEEE
    • Zhang, M.; McCarthy, Z.; Finn, C.; Levine, S.; and Abbeel, P. 2016. Learning deep neural network policies with continuous memory states. In ICRA, 520-527. IEEE.
    • (2016) ICRA , pp. 520-527
    • Zhang, M.1    McCarthy, Z.2    Finn, C.3    Levine, S.4    Abbeel, P.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.