A Mean-Field Game for a Forward-Backward Stochastic System with Partial Observation and Common Noise

Huang, Pengyan; Wang, Guangchen; Wang, Shujun; Xiao, Hua

doi:10.1109/jas.2023.124047

IEEE/CAA J. Autom. Sinica

2024

DOI: 10.1109/jas.2023.124047

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A Mean-Field Game for a Forward-Backward Stochastic System with Partial Observation and Common Noise

Pengyan Huang,

Guangchen Wang,

Shujun Wang

et al.

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2024

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The separation theorem for mean-field linear quadratic optimal control problem with partial information

Song

2024

J. Phys.: Conf. Ser.

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We investigate the separation principle for a class of mean-field stochastic optimal control problems with partial information, where the linear stochastic dynamic system is observed through a linear noisy channel. In our model, the time inconsistency caused by the mean-field terms makes the dynamic programming principle (DPP) inapplicable, which immediately leads to difficulties in deriving the separation principle. Utilizing the optimal filtering equations in several dimensions, we can transform the stochastic optimal control problem with partial information into one with complete details. Subsequently, the Hamilton-Jacobi-Bellman (HJB) equation that can solve the optimal control is obtained with the support of the dynamic programming principle. The separation principle for solving the optimal control of the original problem is derived.

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The separation theorem for mean-field linear quadratic optimal control problem with partial information

Song

2024

J. Phys.: Conf. Ser.

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A Mean-Field Game for a Forward-Backward Stochastic System with Partial Observation and Common Noise

Cited by 1 publication

References 37 publications

The separation theorem for mean-field linear quadratic optimal control problem with partial information

The separation theorem for mean-field linear quadratic optimal control problem with partial information

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