3 papers
stat.ML2026
Model-based Bootstrap for Offline Policy Evaluation in Tabular Reinforcement Learning
Weiwei Wang, Yuqiang Li, Xianyi Wu +1
Offline policy evaluation (OPE) is crucial in high-stakes reinforcement learning applications, where new policies must be assessed reliably before deployment. In such settings, poi…
cs.LG2024
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
Jing Zhang, Linjiajie Fang, Kexin Shi +2
``Distribution shift'' is the main obstacle to the success of offline reinforcement learning. A learning policy may take actions beyond the behavior policy's knowledge, referred to…
cs.LG2024
Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning
Linjiajie Fang, Ruoxue Liu, Jing Zhang +2
In offline reinforcement learning, it is necessary to manage out-of-distribution actions to prevent overestimation of value functions. One class of methods, the policy-regularized…