The paper claims that trust-region-triggered clipping policy optimization with a scaled dot-product graph attention encoder improves RL-based causal discovery on synthetic and benchmark datasets.
Policy gradient methods for reinforcement learning with function approxima- tion,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2024 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Graph-attention-based Casual Discovery with Trust Region-navigated Clipping Policy Optimization
The paper claims that trust-region-triggered clipping policy optimization with a scaled dot-product graph attention encoder improves RL-based causal discovery on synthetic and benchmark datasets.