Search Results for author:
Found 4 papers, 2 papers with code
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback.
Skill-based Meta-Reinforcement Learning
While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible.
Meta Dropout: Learning to Perturb Latent Features for Generalization
Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner.
Ranked #1 on
Meta-Learning
on OMNIGLOT - 1-Shot, 20-way
Meta Dropout: Learning to Perturb Features for Generalization
Specifically, we meta-learn a noise generator which outputs a multiplicative noise distribution for latent features, to obtain low errors on the test instances in an input-dependent manner.
