355 citations · 730 across the 16 of their papers we have counts for
Showing 2020Show all
2 papers · 1 filter
cs.LG2020★ 8 cited
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Andrew Bennett, Nathan Kallus, Lihong Li +1
Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very sam…
cs.LG2020★ 11 cited
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning
Ali Mousavi, Lihong Li, Qiang Liu +1
Off-policy estimation for long-horizon problems is important in many real-life applications such as healthcare and robotics, where high-fidelity simulators may not be available and…