Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.
Stein variational goal generation for adaptive exploration in multi-goal reinforcement learning.arXiv preprint arXiv:2206.06719
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Improving Zero-Shot Offline RL via Behavioral Task Sampling
Extracting task vectors from the offline dataset for policy training improves zero-shot offline RL performance by an average of 20% over random sampling baselines.