39 citations · 44 across the 17 of their papers we have counts for
3 papers · 1 filter
Model Based Meta Learning of Critics for Policy Gradients
Sarah Bechtle, Ludovic Righetti, Franziska Meier
Being able to seamlessly generalize across different tasks is fundamental for robots to act in our world. However, learning representations that generalize quickly to new scenarios…
Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
Armand Jordana, Justin Carpentier, Ludovic Righetti
Modeling dynamical systems plays a crucial role in capturing and understanding complex physical phenomena. When physical models are not sufficiently accurate or hardly describable…
Meta-Learning via Learned Loss
Sarah Bechtle, Artem Molchanov, Yevgen Chebotar +4
Typically, loss functions, regularization mechanisms and other important aspects of training parametric models are chosen heuristically from a limited set of options. In this paper…