Showing cs.LGShow all
2 papers · 1 filter
cs.LG2022
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…
cs.LG2019
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…