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Found 5 papers, 1 papers with code
Goal-Conditioned Offline Reinforcement Learning via Metric Learning
Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets.
Learning Geometric Representations of Objects via Interaction
We address the problem of learning representations from observations of a scene involving an agent and an external object the agent interacts with.
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics
We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties.
Back to the Manifold: Recovering from Out-of-Distribution States
However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time.
On the Subspace Structure of Gradient-Based Meta-Learning
In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.
