Data‐Driven Stochastic Game Theoretic Differential Dynamic Programming

ABSTRACT This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming (SGT‐DDP). The proposed method addresses unknown stochastic systems by approximating both drift and diffusion dynamics. The drift dynamics is est...

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Published in:	International journal of robust and nonlinear control Vol. 35; no. 13; pp. 5343 - 5354
Main Authors:	Sarbaz, Mohammad, Sun, Wei
Format:	Journal Article
Language:	English
Published:	Hoboken, USA John Wiley & Sons, Inc 10.09.2025 Wiley Subscription Services, Inc
Subjects:	Approximation differential dynamic programming Drift estimation Dynamic programming Dynamical systems Game theory Gaussian process gaussian process regression Nonlinear systems Optimal control stochastic dynamics Stochastic systems
ISSN:	1049-8923, 1099-1239
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Abstract	ABSTRACT This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming (SGT‐DDP). The proposed method addresses unknown stochastic systems by approximating both drift and diffusion dynamics. The drift dynamics is estimated via Gaussian Process Regression (GPR) using input–output data. The diffusion dynamics is approximated from the noise data, which is extracted through subtracting the noisy output from the smoothed output. Subsequently, the binning method is combined with GPR to obtain the approximate model of the diffusion dynamics. These approximations are integrated into the SGT‐DDP framework to compute optimal control policies. Simulations on benchmark nonlinear systems under unknown dynamics demonstrate the effectiveness of the method.
AbstractList	This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming (SGT‐DDP). The proposed method addresses unknown stochastic systems by approximating both drift and diffusion dynamics. The drift dynamics is estimated via Gaussian Process Regression (GPR) using input–output data. The diffusion dynamics is approximated from the noise data, which is extracted through subtracting the noisy output from the smoothed output. Subsequently, the binning method is combined with GPR to obtain the approximate model of the diffusion dynamics. These approximations are integrated into the SGT‐DDP framework to compute optimal control policies. Simulations on benchmark nonlinear systems under unknown dynamics demonstrate the effectiveness of the method. ABSTRACT This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming (SGT‐DDP). The proposed method addresses unknown stochastic systems by approximating both drift and diffusion dynamics. The drift dynamics is estimated via Gaussian Process Regression (GPR) using input–output data. The diffusion dynamics is approximated from the noise data, which is extracted through subtracting the noisy output from the smoothed output. Subsequently, the binning method is combined with GPR to obtain the approximate model of the diffusion dynamics. These approximations are integrated into the SGT‐DDP framework to compute optimal control policies. Simulations on benchmark nonlinear systems under unknown dynamics demonstrate the effectiveness of the method.
Author	Sun, Wei Sarbaz, Mohammad
Author_xml	– sequence: 1 givenname: Mohammad orcidid: 0009-0006-8637-1284 surname: Sarbaz fullname: Sarbaz, Mohammad organization: University of Oklahoma – sequence: 2 givenname: Wei surname: Sun fullname: Sun, Wei email: wsun@ou.edu organization: University of Oklahoma
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SubjectTerms	Approximation differential dynamic programming Drift estimation Dynamic programming Dynamical systems Game theory Gaussian process gaussian process regression Nonlinear systems Optimal control stochastic dynamics Stochastic systems
Title	Data‐Driven Stochastic Game Theoretic Differential Dynamic Programming
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