GeoWind2Plan predicts mission-time 3D urban wind via neural operators to enable energy-efficient UAV planning in seconds, reducing energy by up to 12.7% versus wind-agnostic paths.
Strategic risk aversion acts as an inductive bias for generalizable collaboration, yielding robust multi-agent policies with reduced free-riding and stronger equilibrium outcomes alongside unseen partners.
Quantal-response feedback yields logarithmic sample-complexity utility learning up to affine equivalence, while best-response feedback permits only partial identification; an online algorithm achieves low deviation-regret under both models.
Strategic decision-focused learning predicts exogenous states for multi-agent games where better accuracy can reduce equilibrium payoffs, requiring strategic-aware predictors.