3 papers
cs.LG2025
Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline reinforcement learning (RL) aims to learn an effective policy from a static dataset. To alleviate extrapolation errors, existing studies often uniformly regularize the valu…
cs.LG2024
Optimistic Critic Reconstruction and Constrained Fine-Tuning for General Offline-to-Online RL
Qin-Wen Luo, Ming-Kun Xie, Ye-Wen Wang +1
Offline-to-online (O2O) reinforcement learning (RL) provides an effective means of leveraging an offline pre-trained policy as initialization to improve performance rapidly with li…
cs.AI2024
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation
Ye-Wen Wang, Chen-Chen Zong, Ming-Kun Xie +1
Active learning (AL) has achieved great success by selecting the most valuable examples from unlabeled data. However, they usually deteriorate in real scenarios where open-set nois…