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Title: Linear-quadratic-Gaussian mean-field-game with partial observation and common noise
This paper considers a class of linear-quadratic-Gaussian (LQG) mean-field games (MFGs) with partial observation structure for individual agents. Unlike other literature, there are some special features in our formulation. First, the individual state is driven by some common-noise due to the external factor and the state-average thus becomes a random process instead of a deterministic quantity. Second, the sensor function of individual observation depends on state-average thus the agents are coupled in triple manner: not only in their states and cost functionals, but also through their observation mechanism. The decentralized strategies for individual agents are derived by the Kalman filtering and separation principle. The consistency condition is obtained which is equivalent to the wellposedness of some forward-backward stochastic differential equation (FBSDE) driven by common noise. Finally, the related ϵ-Nash equilibrium property is verified.  more » « less
Award ID(s):
1905449
NSF-PAR ID:
10276122
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Mathematical control and related fields
Volume:
11
Issue:
1
ISSN:
2156-8499
Page Range / eLocation ID:
23-46
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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