44 citations · 149 across the 16 of their papers we have counts for
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cs.LG2018★ 22 cited
Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters
Aniruddh Raghu, Omer Gottesman, Yao Liu +4
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empi…
cs.LG2018★ 6 cited
When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms
Yao Liu, Emma Brunskill
Efficient exploration is one of the key challenges for reinforcement learning (RL) algorithms. Most traditional sample efficiency bounds require strategic exploration. Recently man…
cs.LG2018★ 8 cited
Representation Balancing MDPs for Off-Policy Policy Evaluation
Yao Liu, Omer Gottesman, Aniruddh Raghu +4
We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value…