Approximate stochastic control based on deep learning and forward backward stochastic differential equations

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dc.contributor.authorAndersson, Kristoffer
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.departmentChalmers University of Technology / Department of Mathematical Sciencesen
dc.date.accessioned2019-07-03T14:58:44Z
dc.date.available2019-07-03T14:58:44Z
dc.date.issued2019
dc.description.abstractIn this thesis numerical methods for stochastic optimal control are investigated. More precisely a nonlinear Gaussian diffusion type state equation with control in the drift and a quadratic cost functional with finite time horizon is considered. The proposed algorithm relies on recent work on deep learning based solvers for backward stochastic differential equations. The stochastic optimal control problem is reformulated as a forward backward stochastic differential equation and the algorithm is modified to apply to the problem of this thesis. The algorithm is tested on the benchmark problems of controlling single and double inverted pendulums on a cart. It is shown by numerical experiments that the algorithm performs well on both examples.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256458
dc.language.isoeng
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectMatematik
dc.subjectMathematics
dc.titleApproximate stochastic control based on deep learning and forward backward stochastic differential equations
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeEngineering mathematics and computational science (MPENM), MSc
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