-
2
-
-
84965150792
-
A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems
-
E. Todorov and W. Li. A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. In ACC. IEEE, 2005.
-
(2005)
ACC. IEEE
-
-
Todorov, E.1
Li, W.2
-
3
-
-
85161959352
-
Receding horizon differential dynamic programming
-
Y. Tassa, T. Erez, and W. D. Smart. Receding horizon differential dynamic programming. In Proc. of NIPS, 2008.
-
(2008)
Proc. of NIPS
-
-
Tassa, Y.1
Erez, T.2
Smart, W.D.3
-
4
-
-
84937825341
-
Probabilistic differential dynamic programming
-
Y. Pan and E. Theodorou. Probabilistic differential dynamic programming. In Proc. of NIPS, 2014.
-
(2014)
Proc. of NIPS
-
-
Pan, Y.1
Theodorou, E.2
-
5
-
-
84898932265
-
Variational policy search via trajectory optimization
-
S. Levine and V. Koltun. Variational policy search via trajectory optimization. In Proc. of NIPS, 2013.
-
(2013)
Proc. of NIPS
-
-
Levine, S.1
Koltun, V.2
-
7
-
-
84919796093
-
Stochastic backpropagation and approximate inference in deep generative models
-
D. J. Rezende, S. Mohamed, and D. Wierstra. Stochastic backpropagation and approximate inference in deep generative models. In Proc. of ICML, 2014.
-
(2014)
Proc. of ICML
-
-
Rezende, D.J.1
Mohamed, S.2
Wierstra, D.3
-
11
-
-
17444424051
-
Iterative linear quadratic regulator design for nonlinear biological movement systems
-
W. Li and E. Todorov. Iterative Linear Quadratic Regulator Design for Nonlinear Biological Movement Systems. In Proc. of ICINCO, 2004.
-
(2004)
Proc. of ICINCO
-
-
Li, W.1
Todorov, E.2
-
12
-
-
71149083296
-
Robot trajectory optimization using approximate inference
-
M. Toussaint. Robot Trajectory Optimization using Approximate Inference. In Proc. of ICML, 2009.
-
(2009)
Proc. of ICML
-
-
Toussaint, M.1
-
14
-
-
85083951076
-
Adam: A method for stochastic optimization
-
D. Kingma and J. Ba. Adam: A method for stochastic optimization. In Proc. of ICLR, 2015.
-
(2015)
Proc. of ICLR
-
-
Kingma, D.1
Ba, J.2
-
15
-
-
0030082891
-
An approach to fuzzy control of nonlinear systems; stability and design issues
-
H. Wang, K. Tanaka, and M. Griffin. An approach to fuzzy control of nonlinear systems; stability and design issues. IEEE Trans. on Fuzzy Systems, 4(1), 1996.
-
(1996)
IEEE Trans. on Fuzzy Systems
, vol.4
, Issue.1
-
-
Wang, H.1
Tanaka, K.2
Griffin, M.3
-
16
-
-
0003420416
-
-
MIT Press, Cambridge, MA, USA, 1st edition
-
R. S. Sutton and A. G. Barto. Introduction to Reinforcement Learning. MIT Press, Cambridge, MA, USA, 1st edition, 1998. ISBN 0262193981.
-
(1998)
Introduction to Reinforcement Learning
-
-
Sutton, R.S.1
Barto, A.G.2
-
17
-
-
85087991588
-
Autonomous learning of state representations for control
-
W. Böhmer, J. T. Springenberg, J. Boedecker, M. Riedmiller, and K. Obermayer. Autonomous learning of state representations for control. KI-Künstliche Intelligenz, 2015.
-
(2015)
KI-Künstliche Intelligenz
-
-
Böhmer, W.1
Springenberg, J.T.2
Boedecker, J.3
Riedmiller, M.4
Obermayer, K.5
-
18
-
-
79959451979
-
Deep auto-encoder neural networks in reinforcement learning
-
S. Lange and M. Riedmiller. Deep auto-encoder neural networks in reinforcement learning. In Proc. of IJCNN, 2010.
-
(2010)
Proc. of IJCNN
-
-
Lange, S.1
Riedmiller, M.2
-
19
-
-
84924051598
-
Human-level control through deep reinforcement learning
-
02
-
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540), 02 2015.
-
(2015)
Nature
, vol.518
, Issue.7540
-
-
Mnih, V.1
Kavukcuoglu, K.2
Silver, D.3
Rusu, A.A.4
Veness, J.5
Bellemare, M.G.6
Graves, A.7
Riedmiller, M.8
Fidjeland, A.K.9
Ostrovski, G.10
Petersen, S.11
Beattie, C.12
Sadik, A.13
Antonoglou, I.14
King, H.15
Kumaran, D.16
Wierstra, D.17
Legg, S.18
Hassabis, D.19
-
20
-
-
84962230791
-
Learning of non-parametric control policies with highdimensional state features
-
H. van Hoof, J. Peters, and G. Neumann. Learning of non-parametric control policies with highdimensional state features. In Proc. of AISTATS, 2015.
-
(2015)
Proc. of AISTATS
-
-
Van Hoof, H.1
Peters, J.2
Neumann, G.3
-
21
-
-
84943767635
-
-
CoRR, abs/1504.00702
-
S. Levine, C. Finn, T. Darrell, and P. Abbeel. End-to-end training of deep visuomotor policies. CoRR, abs/1504.00702, 2015. URL http://arxiv.org/abs/1504.00702.
-
(2015)
End-to-end Training of Deep Visuomotor Policies
-
-
Levine, S.1
Finn, C.2
Darrell, T.3
Abbeel, P.4
-
23
-
-
84983208884
-
DRAW: A recurrent neural network for image generation
-
K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra. DRAW: A recurrent neural network for image generation. In Proc. of ICML, 2015.
-
(2015)
Proc. of ICML
-
-
Gregor, K.1
Danihelka, I.2
Graves, A.3
Rezende, D.4
Wierstra, D.5
-
27
-
-
85083950051
-
Transformation properties of learned visual representations
-
T. Cohen and M. Welling. Transformation properties of learned visual representations. In ICLR, 2015.
-
(2015)
ICLR
-
-
Cohen, T.1
Welling, M.2
-
29
-
-
84879878460
-
Learning to relate images
-
R. Memisevic. Learning to relate images. IEEE Trans. on PAMI, 35(8):1829-1846, 2013.
-
(2013)
IEEE Trans. on PAMI
, vol.35
, Issue.8
, pp. 1829-1846
-
-
Memisevic, R.1
-
32
-
-
84923272712
-
Latent Kullback Leibler control for continuous-state systems using probabilistic graphical models
-
T. Matsubara, V. Gómez, and H. J. Kappen. Latent Kullback Leibler control for continuous-state systems using probabilistic graphical models. UAI, 2014.
-
(2014)
UAI
-
-
Matsubara, T.1
Gómez, V.2
Kappen, H.J.3
|