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Volume 33, Issue 4
A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters

Richard Archibald, Feng Bao & Jiongmin Yong

DOI: 10.4208/cicp.OA-2022-0310

Commun. Comput. Phys., 33 (2023), pp. 1132-1163.

Published online: 2023-05

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  • 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.

  • Keywords

Stochastic optimal control, parameter estimation, optimal filter, backward stochastic differential equations, stochastic gradient descent.

  • AMS Subject Headings

93E11, 60G35, 65K10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-33-1132, author = {Archibald , RichardBao , Feng and Yong , Jiongmin}, title = {A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters}, journal = {Communications in Computational Physics}, year = {2023}, volume = {33}, number = {4}, pages = {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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2022-0310}, url = {http://global-sci.org/intro/article_detail/cicp/21672.html} }
TY - JOUR T1 - A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters AU - Archibald , Richard AU - Bao , Feng AU - Yong , Jiongmin JO - Communications in Computational Physics VL - 4 SP - 1132 EP - 1163 PY - 2023 DA - 2023/05 SN - 33 DO - http://doi.org/10.4208/cicp.OA-2022-0310 UR - https://global-sci.org/intro/article_detail/cicp/21672.html KW - Stochastic optimal control, parameter estimation, optimal filter, backward stochastic differential equations, stochastic gradient descent. AB -

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.

Archibald , RichardBao , Feng and Yong , Jiongmin. (2023). A Sample-Wise Data Driven Control Solver for the Stochastic Optimal Control Problem with Unknown Model Parameters. Communications in Computational Physics. 33 (4). 1132-1163. doi:10.4208/cicp.OA-2022-0310
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