88 citations · 407 across the 41 of their papers we have counts for
4 papers · 1 filter
Generalized Inner Loop Meta-Learning
Edward Grefenstette, Brandon Amos, Denis Yarats +6
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimiza…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
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…
Curious iLQR: Resolving Uncertainty in Model-based RL
Sarah Bechtle, Yixin Lin, Akshara Rai +2
Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for lear…