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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 126–150 of 265 papers

TitleStatusHype
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Scalable and Robust Self-Learning for Skill Routing in Large-Scale Conversational AI Systems—0
Off-Policy Evaluation with Online Adaptation for Robot Exploration in Challenging Environments—0
Model-Free and Model-Based Policy Evaluation when Causality is UncertainCode0
Marginalized Operators for Off-policy Reinforcement Learning—0
Bellman Residual Orthogonalization for Offline Reinforcement Learning—0
Off-Policy Evaluation in Embedded Spaces—0
Off-Policy Evaluation with Policy-Dependent Optimization Response—0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Doubly Robust Distributionally Robust Off-Policy Evaluation and LearningCode1
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Generalizing Off-Policy Evaluation From a Causal Perspective For Sequential Decision-Making—0
On Well-posedness and Minimax Optimal Rates of Nonparametric Q-function Estimation in Off-policy Evaluation—0
Off-Policy Evaluation Using Information Borrowing and Context-Based SwitchingCode0
Optimal discharge of patients from intensive care via a data-driven policy learning framework—0
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Weighted model estimation for offline model-based reinforcement learning—0
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and LearningCode0
Loss Functions for Discrete Contextual Pricing with Observational Data—0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
SOPE: Spectrum of Off-Policy EstimatorsCode0
Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision ProcessesCode0
Towards Hyperparameter-free Policy Selection for Offline Reinforcement LearningCode0
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