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2022 ICML ICML 2022

Safe Exploration for Efficient Policy Evaluation and Comparison

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.

🌉 Interdisciplinary Bridge — Machine Learning and Reinforcement Learning
🐣 Hot Topic Early Bird — policy evaluation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Runzhe Wan , Branislav Kveton , Rui Song

Topics

Machine Learning > Optimization & Theory > Optimization Reinforcement Learning > Methods > Policy Learning Machine Learning > Learning Types > Reinforcement Learning Machine Learning > Optimization & Theory > Online Algorithms Machine Learning > Learning Types > Multi-Armed Bandits

Keywords

policy evaluation exploration policy multi-armed bandit data collection safe exploration
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