CENIE augments regret-based unsupervised environment design with a GMM-based novelty score derived from the student's state-action coverage, improving zero-shot transfer in Minigrid, BipedalWalker, and CarRacing.
PyTorch Adapt
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
PyTorch Adapt is a library for domain adaptation, a type of machine learning algorithm that re-purposes existing models to work in new domains. It is a fully-featured toolkit, allowing users to create a complete train/test pipeline in a few lines of code. It is also modular, so users can import just the parts they need, and not worry about being locked into a framework. One defining feature of this library is its customizability. In particular, complex training algorithms can be easily modified and combined, thanks to a system of composable, lazily-evaluated hooks. In this technical report, we explain in detail these features and the overall design of the library. Code is available at https://www.github.com/KevinMusgrave/pytorch-adapt
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
CENIE augments regret-based unsupervised environment design with a GMM-based novelty score derived from the student's state-action coverage, improving zero-shot transfer in Minigrid, BipedalWalker, and CarRacing.