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Found 4 papers, 2 papers with code
Steerable Equivariant Representation Learning
In this paper, we propose a method of learning representations that are instead equivariant to data augmentations.
Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning
Bilevel planning, in which a high-level search over an abstraction of an environment is used to guide low-level decision-making, is an effective approach to solving long-horizon tasks in continuous state and action spaces.
Predicate Invention for Bilevel Planning
Our key idea is to learn predicates by optimizing a surrogate objective that is tractable but faithful to our real efficient-planning objective.
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning
Sparse rewards and long time horizons remain challenging for reinforcement learning algorithms.
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