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Results: 1 to 20 of 108

Similar articles for PubMed (Select 24491826)

1.

Policy oscillation is overshooting.

Wagner P.

Neural Netw. 2014 Apr;52:43-61. doi: 10.1016/j.neunet.2014.01.002. Epub 2014 Jan 21.

PMID:
24491826
2.

Reinforcement learning in continuous time and space.

Doya K.

Neural Comput. 2000 Jan;12(1):219-45.

PMID:
10636940
3.

Kernel-based least squares policy iteration for reinforcement learning.

Xu X, Hu D, Lu X.

IEEE Trans Neural Netw. 2007 Jul;18(4):973-92.

PMID:
17668655
4.

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

Lewis FL, Vamvoudakis KG.

IEEE Trans Syst Man Cybern B Cybern. 2011 Feb;41(1):14-25. doi: 10.1109/TSMCB.2010.2043839. Epub 2010 Mar 29.

PMID:
20350860
5.
6.

Autonomous reinforcement learning with experience replay.

Wawrzyński P, Tanwani AK.

Neural Netw. 2013 May;41:156-67. doi: 10.1016/j.neunet.2012.11.007. Epub 2012 Nov 29.

PMID:
23237972
7.

Reinforcement learning of motor skills with policy gradients.

Peters J, Schaal S.

Neural Netw. 2008 May;21(4):682-97. doi: 10.1016/j.neunet.2008.02.003. Epub 2008 Apr 26.

PMID:
18482830
8.

Adaptive importance sampling for value function approximation in off-policy reinforcement learning.

Hachiya H, Akiyama T, Sugiayma M, Peters J.

Neural Netw. 2009 Dec;22(10):1399-410. doi: 10.1016/j.neunet.2009.01.002. Epub 2009 Jan 23.

PMID:
19216050
9.

Adaptive dynamic programming approach to experience-based systems identification and control.

Lendaris GG.

Neural Netw. 2009 Jul-Aug;22(5-6):822-32. doi: 10.1016/j.neunet.2009.06.021. Epub 2009 Jul 2.

PMID:
19632087
10.

Partially observable Markov decision processes and performance sensitivity analysis.

Li Y, Yin B, Xi H.

IEEE Trans Syst Man Cybern B Cybern. 2008 Dec;38(6):1645-51. doi: 10.1109/TSMCB.2008.927711.

PMID:
19022734
11.

Approximate dynamic programming for optimal stationary control with control-dependent noise.

Jiang Y, Jiang ZP.

IEEE Trans Neural Netw. 2011 Dec;22(12):2392-8. doi: 10.1109/TNN.2011.2165729. Epub 2011 Sep 26.

PMID:
21954203
12.

Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent.

Wang Y, Christley S, Mjolsness E, Xie X.

BMC Syst Biol. 2010 Jul 21;4:99. doi: 10.1186/1752-0509-4-99.

13.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

Zhang H, Song R, Wei Q, Zhang T.

IEEE Trans Neural Netw. 2011 Dec;22(12):1851-62. doi: 10.1109/TNN.2011.2172628. Epub 2011 Nov 1.

PMID:
22057063
14.

Composition of web services using Markov decision processes and dynamic programming.

Uc-Cetina V, Moo-Mena F, Hernandez-Ucan R.

ScientificWorldJournal. 2015;2015:545308. doi: 10.1155/2015/545308. Epub 2015 Mar 22.

15.

Reinforcement learning for resource allocation in LEO satellite networks.

Usaha W, Barria JA.

IEEE Trans Syst Man Cybern B Cybern. 2007 Jun;37(3):515-27.

PMID:
17550108
16.

Intervention in gene regulatory networks via greedy control policies based on long-run behavior.

Qian X, Ivanov I, Ghaffari N, Dougherty ER.

BMC Syst Biol. 2009 Jun 15;3:61. doi: 10.1186/1752-0509-3-61.

17.

Meta-learning in reinforcement learning.

Schweighofer N, Doya K.

Neural Netw. 2003 Jan;16(1):5-9.

PMID:
12576101
18.

Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound.

Wang FY, Jin N, Liu D, Wei Q.

IEEE Trans Neural Netw. 2011 Jan;22(1):24-36. doi: 10.1109/TNN.2010.2076370. Epub 2010 Sep 27.

PMID:
20876014
19.

Parameter-exploring policy gradients.

Sehnke F, Osendorfer C, Rückstiess T, Graves A, Peters J, Schmidhuber J.

Neural Netw. 2010 May;23(4):551-9. doi: 10.1016/j.neunet.2009.12.004. Epub 2009 Dec 16.

PMID:
20061118
20.

Derivatives of logarithmic stationary distributions for policy gradient reinforcement learning.

Morimura T, Uchibe E, Yoshimoto J, Peters J, Doya K.

Neural Comput. 2010 Feb;22(2):342-76. doi: 10.1162/neco.2009.12-08-922.

PMID:
19842990
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