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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| Vydáno v: | International journal of robust and nonlinear control Ročník 35; číslo 13; s. 5343 - 5354 |
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Hoboken, USA
John Wiley & Sons, Inc
10.09.2025
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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. |
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| 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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This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming... This paper introduces a novel approach for designing optimal control using data‐driven Stochastic Game Theoretic Differential Dynamic Programming (SGT‐DDP).... |
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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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