Niklas Schmid

Model-Agnostic Meta Learning for Differentiable MPC

This work proposes a novel framework combining policy optimization with meta-learning to train highly adaptable MPC controllers, which enables rapid adaptation to unseen tasks, maintaining high performance at a fraction of the computational cost required for full retraining.