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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 1–25 of 265 papers

TitleStatusHype
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Trajectory World Models for Heterogeneous EnvironmentsCode1
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy EvaluationCode1
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy EvaluationCode1
Off-Policy Evaluation of Ranking Policies under Diverse User BehaviorCode1
Anytime-valid off-policy inference for contextual banditsCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Doubly Robust Distributionally Robust Off-Policy Evaluation and LearningCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Evaluating the Robustness of Off-Policy EvaluationCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy EvaluationCode1
Active Offline Policy SelectionCode1
Offline RL Without Off-Policy EvaluationCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Benchmarks for Deep Off-Policy EvaluationCode1
Optimal Off-Policy Evaluation from Multiple Logging PoliciesCode1
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy EvaluationCode1
Counterfactual Evaluation of Slate Recommendations with Sequential Reward InteractionsCode1
Off-Policy Evaluation and Learning for the Future under Non-Stationarity—0
A Principled Path to Fitted Distributional Evaluation—0
Semi-gradient DICE for Offline Constrained Reinforcement Learning—0
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