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Stochastic linear-quadratic differential game with Markovian jumps in an infinite horizon
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Stochastic linear-quadratic differential game with Markovian jumps in an infinite horizon
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This paper investigates a two-person non-homogeneous linear-quadratic stochastic differential game (LQ-SDG, for short) in an infinite horizon for a system regulated by a time-invariant Markov chain. Both non-zero-sum and zero-sum LQ-SDG problems are studied. It is shown that the zero-sum LQ-SDG problem can be considered a special non-zero-sum LQ-SDG problem. The open-loop Nash equilibrium point of non-zero-sum (zero-sum, respectively) LQ-SDG problem is characterized by the solvability of a system of constrained forward-backward stochastic differential equations (FBSDEs, for short) in an infinite horizon and the convexity (convexity-concavity, respectively) of the performance functional and their corresponding closed-loop Nash equilibrium strategy are characterized by the solvability of a system of constrained coupled algebra Riccati equations (CAREs, for short) with certain stabilizing conditions. In addition, the closed-loop representation of open-loop Nash equilibrium point for non-zero-sum (zero-sum, respectively) LQ-SDG is provided by the non-symmetric (symmetric, respectively) solution to a system CAREs. At the end of this paper, we provide three concrete examples and solve their open-loop/closed-loop Nash equilibrium strategy based on the obtained results.
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Cited by 1 Pith paper
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Turnpike properties for zero-sum stochastic linear quadratic differential games of Markovian regime switching system
Finite-horizon optimal feedback gains in zero-sum stochastic linear-quadratic games with regime switching converge exponentially to infinite-horizon gains, yielding a turnpike theorem for the optimal triple.
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