44 citations · 149 across the 16 of their papers we have counts for
4 papers · 1 filter
All-Action Policy Gradient Methods: A Numerical Integration Approach
Benjamin Petit, Loren Amdahl-Culleton, Yao Liu +2
While often stated as an instance of the likelihood ratio trick [Rubinstein, 1989], the original policy gradient theorem [Sutton, 1999] involves an integral over the action space.…
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling
Yao Liu, Pierre-Luc Bacon, Emma Brunskill
Off-policy policy estimators that use importance sampling (IS) can suffer from high variance in long-horizon domains, and there has been particular excitement over new IS methods t…
Combining Parametric and Nonparametric Models for Off-Policy Evaluation
Omer Gottesman, Yao Liu, Scott Sussex +2
We consider a model-based approach to perform batch off-policy evaluation in reinforcement learning. Our method takes a mixture-of-experts approach to combine parametric and non-pa…
Off-Policy Policy Gradient with State Distribution Correction
Yao Liu, Adith Swaminathan, Alekh Agarwal +1
We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approac…