A Nonzero-sum game with reinforcement learning under mean-variance framework
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| 001 | MAP20260001968 | ||
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| 040 | $aMAP$bspa$dMAP | ||
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| 245 | 1 | 2 | $aA Nonzero-sum game with reinforcement learning under mean-variance framework$cJunyi Guo...[et al.] |
| 520 | $aThis paper examines a competitive setting in which two agents invest in a risk-free and a risky asset while considering both their own wealth and their wealth gap relative to each other. With market parameters partially or fully unknown, the problem is formulated as a nonzero-sum differential game within a reinforcement learning framework. Each agent seeks to optimize a Choquet-regularized, time-inconsistent mean-variance objective. Using dynamic programming, the authors derive a time-consistent Nash equilibrium in an incomplete market. Under a Gaussian mean-return assumption, they obtain an explicit analytical solution that enables the construction of a practical reinforcement learning algorithm. The algorithm shows uniform convergence, despite the absence of a traditional policy-improvement guarantee, and numerical experiments confirm its robustness and effectiveness | ||
| 650 | 4 | $0MAPA20080597641$aMercados financieros | |
| 650 | 4 | $0MAPA20250003316$aGestión de riesgos | |
| 650 | 4 | $0MAPA20080586447$aModelo estocástico | |
| 650 | 4 | $0MAPA20080576790$aModelo Gaussiano | |
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080602437$aMatemática del seguro | |
| 650 | 4 | $0MAPA20080592042$aModelos matemáticos | |
| 700 | 1 | $0MAPA20080649876$aGuo, Junyi | |
| 710 | 2 | $0MAPA20100017661$aInternational Actuarial Association | |
| 773 | 0 | $wMAP20077000420$g19/01/2026 Volume 56 Issue 1 - January 2026 , p. 154 - 180$x0515-0361$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association |