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Off-policy evaluation

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.

Papers

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Showing 161–170 of 265 papers

TitleStatusHype
Online Learning for Recommendations at Grubhub—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Supervised Off-Policy RankingCode0
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy EvaluationCode1
Variance-Aware Off-Policy Evaluation with Linear Function Approximation—0
Active Offline Policy SelectionCode1
Offline RL Without Off-Policy EvaluationCode1
Control Variates for Slate Off-Policy EvaluationCode0
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
Robust Generalization despite Distribution Shift via Minimum Discriminating InformationCode0
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