Yihao Feng* — accepted AI-conference papers

2020

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation

Ziyang Tang*, Yihao Feng*, Lihong Li, Dengyong Zhou, Qiang Liu

ICLR 2020spotlight

Infinite horizon off-policy policy evaluation is a highly challenging task due to the excessively large variance of typical importance sampling (IS) estimators. Recently, Liu et al. (2018) proposed an approach that significantly reduces the variance of infinite-horizon off-policy evaluation by estim…

Cited by 78 Source ↗Scholar ↗

2018

Action-dependent Control Variates for Policy Optimization via Stein Identity

Hao Liu*, Yihao Feng*, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

ICLR 2018poster

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control va…

Cited by 100 Source ↗PDF ↗Scholar ↗