Inverse Linear-Quadratic Gaussian Differential Games
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914514127028224 |
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| author | Günther, Lucas Thömmes, Felix Handwerker, Karl Varga, Balint Hohmann, Sören |
| author_facet | Günther, Lucas Thömmes, Felix Handwerker, Karl Varga, Balint Hohmann, Sören |
| contents | This paper presents a method for solving the Inverse Stochastic Differential Game (ISDG) problem in finite-horizon linear-quadratic Gaussian (LQG) differential games. The objective is to recover cost function parameters of all players, as well as noise scaling parameters of the stochastic system, consistent with observed trajectories. The proposed framework combines (i) estimation of the feedback strategies, (ii) identification of the cost function parameters via a novel reformulation of the coupled Riccati differential equations, and (iii) maximum likelihood estimation of the noise scaling parameters. Simulation results demonstrate that the approach recovers parameters, yielding trajectories that closely match the observed trajectories. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_05552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Inverse Linear-Quadratic Gaussian Differential Games Günther, Lucas Thömmes, Felix Handwerker, Karl Varga, Balint Hohmann, Sören Systems and Control This paper presents a method for solving the Inverse Stochastic Differential Game (ISDG) problem in finite-horizon linear-quadratic Gaussian (LQG) differential games. The objective is to recover cost function parameters of all players, as well as noise scaling parameters of the stochastic system, consistent with observed trajectories. The proposed framework combines (i) estimation of the feedback strategies, (ii) identification of the cost function parameters via a novel reformulation of the coupled Riccati differential equations, and (iii) maximum likelihood estimation of the noise scaling parameters. Simulation results demonstrate that the approach recovers parameters, yielding trajectories that closely match the observed trajectories. |
| title | Inverse Linear-Quadratic Gaussian Differential Games |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.05552 |