2 papers
stat.ML2026
Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions
Zeyu Bian, Ying Zhou, Yifan Cui
Standard offline reinforcement learning (RL) algorithms typically assume that the actions in the dataset are observed without error. However, in many real-world applications, the t…
stat.ML2025
Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
Yuhan Li, Eugene Han, Yifan Hu +4
This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces when unmeasured confounders are present. While most existing r…