18 citations · 18 across the 1 of their papers we have counts for
3 papers
cs.LG2021★ 18 cited
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction
Ming Yin, Yu Bai, Yu-Xiang Wang
We consider the problem of offline reinforcement learning (RL) -- a well-motivated setting of RL that aims at policy optimization using only historical data. Despite its wide appli…
cs.LG2020
Near-Optimal Provable Uniform Convergence in Offline Policy Evaluation for Reinforcement Learning
Ming Yin, Yu Bai, Yu-Xiang Wang
The problem of Offline Policy Evaluation (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real-life applications. Existing work on OPE mostly focus on…
cs.LG2020
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning
Ming Yin, Yu-Xiang Wang
We consider the problem of off-policy evaluation for reinforcement learning, where the goal is to estimate the expected reward of a target policy using offline data collected b…