A model-based goal augmentation method, MGDA, improves the stitching ability of offline goal-conditioned weighted supervised learning on maze benchmarks by filtering augmented goals through a locally Lipschitz dynamics model.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning
A model-based goal augmentation method, MGDA, improves the stitching ability of offline goal-conditioned weighted supervised learning on maze benchmarks by filtering augmented goals through a locally Lipschitz dynamics model.