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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 11–20 of 265 papers

TitleStatusHype
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
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