Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset, but its performance is fundamentally limited by dataset coverage. Action preference queries leverage expert feedback without additional environment interaction, enabling policy improvement during offline training. However, existing methods still face two key challenges: selecting informative preference queries and effectively exploiting the collected feedback. Current approaches typically rely only on the distance between policy actions and dataset actions for query selection, while enforcing fixed con
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- FuzzyOverlapping authors or contributors · 78%sgl-project/sglang →
“Shared author/contributor keys: luo, zhou”
- LinkedLinked via arxiv author · 85%Li-Rong Zhou →
“Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation”
- LinkedLinked via arxiv author · 85%Qin-Wen Luo →
“Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation”
- LinkedLinked via arxiv author · 85%Sheng-Jun Huang →
“Conservative Query and Adaptive Regularization for Offline RL Under Uncertainty Estimation”