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Found 8 papers, 2 papers with code
Approximate Equivariance in Reinforcement Learning
Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when there is symmetry in the task.
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws. These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group.
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture.
Robust Imitation of a Few Demonstrations with a Backwards Model
On continuous control domains, we evaluate the robustness when starting from different initial states unseen in the demonstration data.
Learning Symmetric Embeddings for Equivariant World Models
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations.
Learning Symmetric Representations for Equivariant World Models
In this paper, we use equivariant transition models as an inductive bias to learn symmetric latent representations in a self-supervised manner.
Generator Surgery for Compressed Sensing
We introduce a method for achieving low representation error using generators as signal priors.
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science.
