Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems.
reinforcement-learning-rl-1
Off-policy evaluation
3260 papers • 126 benchmarks • 313 datasets
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Most implemented papers
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita•Sun Aug 16 2020
Open Bandit Dataset is presented, a public logged bandit dataset collected on a large-scale fashion e-commerce platform, ZOZOTOWN, that enables experimental comparisons of different OPE estimators for the first time and develops Python software called Open Bandit Pipeline to streamline and standardize the implementation of batch bandit algorithms and OPE.