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.
An application of reinforcement learning to aerobatic helicopter flight.Advances in neural information processing systems, 19, 2006
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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.