A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters
Year: 2023
Author: Richard Archibald, Feng Bao, Jiongmin Yong
Communications in Computational Physics, Vol. 33 (2023), Iss. 4 : pp. 1132–1163
Abstract
In this work, an efficient sample-wise data driven control solver will be developed to solve the stochastic optimal control problem with unknown model parameters. A direct filter method will be applied as an online parameter estimation method that dynamically estimates the target model parameters upon receiving the data, and a sample-wise optimal control solver will be provided to efficiently search for the optimal control. Then, an effective overarching algorithm will be introduced to combine the parameter estimator and the optimal control solver. Numerical experiments will be carried out to demonstrate the effectiveness and the efficiency of the sample-wise data driven control method.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/cicp.OA-2022-0310
Communications in Computational Physics, Vol. 33 (2023), Iss. 4 : pp. 1132–1163
Published online: 2023-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 32
Keywords: Stochastic optimal control parameter estimation optimal filter backward stochastic differential equations stochastic gradient descent.