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dc.contributor.author
Menchón, Silvia Adriana
dc.contributor.author
Kappen, Hilbert Johan
dc.date.available
2019-12-06T17:42:59Z
dc.date.issued
2019-12-21
dc.identifier.citation
Menchón, Silvia Adriana; Kappen, Hilbert Johan; Learning effective state-feedback controllers through efficient multilevel importance samplers; Taylor & Francis Ltd; International Journal Of Control; 92; 12; 21-12-2019; 2776-2783
dc.identifier.issn
0020-7179
dc.identifier.uri
http://hdl.handle.net/11336/91650
dc.description.abstract
Monte Carlo sampling can be used to estimate the solution of path integral control problems, which are a restricted class of nonlinear control problems with arbitrary dynamics and state cost, but with a linear dependence of the control on the dynamics and quadratic control cost. Although importance sampling is used to improve numerical computations, the effective sample size may still be low or many samples could be required. In this work, we propose a method to learn effective state-feedback controllers for nonlinear stochastic control problems based on multilevel importance samplers. In particular, we focus on the question of how to compute effective importance samplers considering a multigrid scenario. We test our algorithm in finite horizon control problems based on Lorenz-96 model with chaotic and non-chaotic behaviour, showing, in all cases, that our multigrid implementation reduces the computational time and improves the effective sample size.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis Ltd
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
IMPORTANCE SAMPLING
dc.subject
MULTILEVEL MONTE CARLO METHOD
dc.subject
PATH INTEGRAL CONTROL PROBLEMS
dc.subject.classification
Otras Ciencias Físicas
dc.subject.classification
Ciencias Físicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Learning effective state-feedback controllers through efficient multilevel importance samplers
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-10-22T16:44:02Z
dc.identifier.eissn
1366-5820
dc.journal.volume
92
dc.journal.number
12
dc.journal.pagination
2776-2783
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Menchón, Silvia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition and Behavior. SNN Machine Learning Group; Países Bajos
dc.description.fil
Fil: Kappen, Hilbert Johan. Radboud Universiteit Nijmegen. Donders Instituto Brain Cognition and Behavior. SNN Machine Learning Group; Países Bajos
dc.journal.title
International Journal Of Control
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1080/00207179.2018.1459857
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/00207179.2018.1459857
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