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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 81–90 of 265 papers

TitleStatusHype
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Combining Parametric and Nonparametric Models for Off-Policy Evaluation—0
A Fast Convergence Theory for Offline Decision Making—0
A Principled Path to Fitted Distributional Evaluation—0
Concept-driven Off Policy Evaluation—0
Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy—0
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits—0
Confident Natural Policy Gradient for Local Planning in q_π-realizable Constrained MDPs—0
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions—0
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