A bilevel optimization framework with normalizing flows recovers the latent obstacle and optimal trajectories in mean-field games from partial trajectory data, achieving about 0.1 relative L2 error even with fewer than 100 trajectories.
A bilevel optimization method for inverse mean-field games*
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Joint Inference of Trajectory and Obstacle in Mean-Field Games via Bilevel Optimization
A bilevel optimization framework with normalizing flows recovers the latent obstacle and optimal trajectories in mean-field games from partial trajectory data, achieving about 0.1 relative L2 error even with fewer than 100 trajectories.