Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan ⋅ Branislav Kveton ⋅ Rui Song
Keywords:
T: Online Learning and Bandits
MISC: Online Learning, Active Learning and Bandits
RL: Batch/Offline
2022 Spotlight
Abstract
High-quality data plays a central role in ensuring the accuracy of policy evaluation. This paper initiates the study of efficient and safe data collection for bandit policy evaluation. We formulate the problem and investigate its several representative variants. For each variant, we analyze its statistical properties, derive the corresponding exploration policy, and design an efficient algorithm for computing it. Both theoretical analysis and experiments support the usefulness of the proposed methods.
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