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Volumn 1, Issue January, 2014, Pages 819-827

Sparse multi-task reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION SCIENCE; ITERATIVE METHODS;

EID: 84937915889     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (74)

References (29)
  • 1
    • 55149088329 scopus 로고    scopus 로고
    • Convex multi-task feature learning
    • Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.
    • (2008) Machine Learning , vol.73 , Issue.3 , pp. 243-272
    • Argyriou, A.1    Evgeniou, T.2    Pontil, M.3
  • 3
    • 68649086910 scopus 로고    scopus 로고
    • Simultaneous analysis of lasso and dantzig selector
    • Peter J Bickel, Ya'acov Ritov, and Alexandre B Tsybakov. Simultaneous analysis of lasso and dantzig selector. The Annals of Statistics, pages 1705-1732, 2009.
    • (2009) The Annals of Statistics , pp. 1705-1732
    • Bickel, P.J.1    Ritov, Y.2    Tsybakov, A.B.3
  • 6
    • 80052251399 scopus 로고    scopus 로고
    • Tree-based feature selection for dimensionality reduction of large-scale control systems
    • A Castelletti, S Galelli, M Restelli, and R Soncini-Sessa. Tree-based feature selection for dimensionality reduction of large-scale control systems. In IEEE ADPRL, 2011.
    • (2011) IEEE ADPRL
    • Castelletti, A.1    Galelli, S.2    Restelli, M.3    Soncini-Sessa, R.4
  • 10
    • 84937834127 scopus 로고    scopus 로고
    • Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information
    • H. Hachiya and M. Sugiyama. Feature selection for reinforcement learning: Evaluating implicit state-reward dependency via conditional mutual information. In ECML PKDD. 2010.
    • (2010) ECML PKDD
    • Hachiya, H.1    Sugiyama, M.2
  • 12
    • 84861687861 scopus 로고    scopus 로고
    • Regularized least squares temporal difference learning with nested ℓ2 and ℓ1 penalization
    • M. Hoffman, A. Lazaric, M. Ghavamzadeh, and R. Munos. Regularized least squares temporal difference learning with nested ℓ2 and ℓ1 penalization. In EWRL, pages 102-114. 2012.
    • (2012) EWRL , pp. 102-114
    • Hoffman, M.1    Lazaric, A.2    Ghavamzadeh, M.3    Munos, R.4
  • 13
    • 71149113559 scopus 로고    scopus 로고
    • Group lasso with overlap and graph lasso
    • ACM
    • Laurent Jacob, Guillaume Obozinski, and Jean-Philippe Vert. Group lasso with overlap and graph lasso. In ICML, pages 433-440. ACM, 2009.
    • (2009) ICML , pp. 433-440
    • Jacob, L.1    Obozinski, G.2    Vert, J.-P.3
  • 14
    • 71149121683 scopus 로고    scopus 로고
    • Regularization and feature selection in least-squares temporal difference learning
    • J. Zico Kolter and Andrew Y. Ng. Regularization and feature selection in least-squares temporal difference learning. In ICML, 2009.
    • (2009) ICML
    • Zico Kolter, J.1    Ng, A.Y.2
  • 15
    • 84883000417 scopus 로고    scopus 로고
    • Transfer in reinforcement learning: A framework and a survey
    • M. Wiering and M. van Otterlo, editors Springer
    • A. Lazaric. Transfer in reinforcement learning: a framework and a survey. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State of the Art. Springer, 2011.
    • (2011) Reinforcement Learning: State of the Art
    • Lazaric, A.1
  • 16
    • 77956497402 scopus 로고    scopus 로고
    • Bayesian multi-task reinforcement learning
    • Alessandro Lazaric and Mohmammad Ghavamzadeh. Bayesian multi-task reinforcement learning. In ICML, 2010.
    • (2010) ICML
    • Lazaric, A.1    Ghavamzadeh, M.2
  • 17
    • 85162564748 scopus 로고    scopus 로고
    • Transfer from multiple MDPs
    • Alessandro Lazaric and Marcello Restelli. Transfer from multiple MDPs. In NIPS, 2011.
    • (2011) NIPS
    • Lazaric, A.1    Restelli, M.2
  • 18
    • 66849131425 scopus 로고    scopus 로고
    • Multi-task reinforcement learning in partially observable stochastic environments
    • Hui Li, Xuejun Liao, and Lawrence Carin. Multi-task reinforcement learning in partially observable stochastic environments. Journal of Machine Learning Research, 10:1131-1186, 2009.
    • (2009) Journal of Machine Learning Research , vol.10 , pp. 1131-1186
    • Li, H.1    Liao, X.2    Carin, L.3
  • 19
    • 84855412474 scopus 로고    scopus 로고
    • Oracle inequalities and optimal inference under group sparsity
    • Karim Lounici, Massimiliano Pontil, Sara Van De Geer, Alexandre B Tsybakov, et al. Oracle inequalities and optimal inference under group sparsity. The Annals of Statistics, 39(4):2164-2204, 2011.
    • (2011) The Annals of Statistics , vol.39 , Issue.4 , pp. 2164-2204
    • Lounici, K.1    Pontil, M.2    Van De Geer, S.3    Tsybakov, A.B.4
  • 21
    • 84867131813 scopus 로고    scopus 로고
    • Greedy algorithms for sparse reinforcement learning
    • C. Painter-Wakefield and R. Parr. Greedy algorithms for sparse reinforcement learning. In ICML, 2012.
    • (2012) ICML
    • Painter-Wakefield, C.1    Parr, R.2
  • 23
    • 84861693137 scopus 로고    scopus 로고
    • Multi-task reinforcement learning: Shaping and feature selection
    • September
    • Matthijs Snel and Shimon Whiteson. Multi-task reinforcement learning: Shaping and feature selection. In EWRL, September 2011.
    • (2011) EWRL
    • Snel, M.1    Whiteson, S.2
  • 25
    • 84863336191 scopus 로고    scopus 로고
    • Multitask reinforcement learning on the distribution of mdps
    • F. Tanaka and M. Yamamura. Multitask reinforcement learning on the distribution of mdps. In CIRA 2003, pages 1108-1113, 2003.
    • (2003) CIRA 2003 , pp. 1108-1113
    • Tanaka, F.1    Yamamura, M.2
  • 26
    • 68949157375 scopus 로고    scopus 로고
    • Transfer learning for reinforcement learning domains: A survey
    • Matthew E. Taylor and Peter Stone. Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(1):1633-1685, 2009.
    • (2009) Journal of Machine Learning Research , vol.10 , Issue.1 , pp. 1633-1685
    • Taylor, M.E.1    Stone, P.2
  • 27
    • 77955054299 scopus 로고    scopus 로고
    • On the conditions used to prove oracle results for the lasso
    • Sara A Van De Geer, Peter Bühlmann, et al. On the conditions used to prove oracle results for the lasso. Electronic Journal of Statistics, 3:1360-1392, 2009.
    • (2009) Electronic Journal of Statistics , vol.3 , pp. 1360-1392
    • Van De Geer, S.A.1    Bühlmann, P.2
  • 28
    • 34547994508 scopus 로고    scopus 로고
    • Multi-task reinforcement learning: A hierarchical Bayesian approach
    • A. Wilson, A. Fern, S. Ray, and P. Tadepalli. Multi-task reinforcement learning: A hierarchical Bayesian approach. In ICML, pages 1015-1022, 2007.
    • (2007) ICML , pp. 1015-1022
    • Wilson, A.1    Fern, A.2    Ray, S.3    Tadepalli, P.4
  • 29
    • 85161985386 scopus 로고    scopus 로고
    • Learning multiple tasks with a sparse matrix-normal penalty
    • Yi Zhang and Jeff G Schneider. Learning multiple tasks with a sparse matrix-normal penalty. In NIPS, pages 2550-2558, 2010.
    • (2010) NIPS , pp. 2550-2558
    • Zhang, Y.1    Schneider, J.G.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.