44 citations · 149 across the 16 of their papers we have counts for
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cs.LG2020★ 35 cited
Provably Good Batch Reinforcement Learning Without Great Exploration
Yao Liu, Adith Swaminathan, Alekh Agarwal +1
Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Doing batch RL in a way that yields a reliable new policy in large domains is chall…
cs.LG2020★ 12 cited
Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Omer Gottesman, Joseph Futoma, Yao Liu +4
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deplo…