Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation

NeurIPS 2018 Qiang LiuLihong LiZiyang TangDengyong Zhou

We consider the off-policy estimation problem of estimating the expected reward of a target policy using samples collected by a different behavior policy. Importance sampling (IS) has been a key technique to derive (nearly) unbiased estimators, but is known to suffer from an excessively high variance in long-horizon problems... (read more)

PDF Abstract NeurIPS 2018 PDF NeurIPS 2018 Abstract

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet