Approximative Policy Iteration for Exit Time Feedback Control Problems Driven by Stochastic Differential Equations using Tensor Train Format
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Publication:5865245
DOI10.1137/20M1372500zbMath1492.93196arXiv2010.04465OpenAlexW3092417881WikidataQ115246873 ScholiaQ115246873MaRDI QIDQ5865245
Konstantin Fackeldey, Leon Sallandt, Reinhold Schneider, Mathias Oster
Publication date: 13 June 2022
Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2010.04465
exit timepolicy iterationvariational Monte CarloKoopman operatortensor trainHamilton-Jacobi-Bellmann (HJB)
Feedback control (93B52) Dynamic programming (90C39) Optimal stochastic control (93E20) Numerical solutions to stochastic differential and integral equations (65C30)
Related Items (4)
Approximating Optimal feedback Controllers of Finite Horizon Control Problems Using Hierarchical Tensor Formats ⋮ Low-rank tensor methods for partial differential equations ⋮ Data-Driven Tensor Train Gradient Cross Approximation for Hamilton–Jacobi–Bellman Equations ⋮ Pricing High-Dimensional Bermudan Options with Hierarchical Tensor Formats
Uses Software
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