2 papers
cs.LG2020
Off-Policy Evaluation of Bandit Algorithm from Dependent Samples under Batch Update Policy
Masahiro Kato, Yusuke Kaneko
The goal of off-policy evaluation (OPE) is to evaluate a new policy using historical data obtained via a behavior policy. However, because the contextual bandit algorithm updates t…
cs.LG2020
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games
Kenshi Abe, Yusuke Kaneko
Off-policy evaluation (OPE) is the problem of evaluating new policies using historical data obtained from a different policy. In the recent OPE context, most studies have focused o…