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off-policy evaluation

176 papers

OPE

Papers

Model-Free and Model-Based Policy Evaluation when Causality is Uncertain ICML 2021 Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings MLHC 2021 Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds ICLR 2021 Offline RL Without Off-Policy Evaluation NeurIPS 2021 Off-Policy Confidence Sequences ICML 2021 Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders AISTATS 2021 Off-Policy Risk Assessment in Contextual Bandits NeurIPS 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021 Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings NeurIPS 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021 SOPE: Spectrum of Off-Policy Estimators NeurIPS 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient ICML 2021 State Relevance for Off-Policy Evaluation ICML 2021 Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning NeurIPS 2021 Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach EMNLP 2021 Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning NeurIPS 2021 Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation NeurIPS 2021 Universal Off-Policy Evaluation NeurIPS 2021 Variance-Aware Off-Policy Evaluation with Linear Function Approximation NeurIPS 2021 Weighted model estimation for offline model-based reinforcement learning NeurIPS 2021 Accountable Off-Policy Evaluation With Kernel Bellman Statistics ICML 2020 Adaptive Estimator Selection for Off-Policy Evaluation ICML 2020 Adaptive Trade-Offs in Off-Policy Learning AISTATS 2020 A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs NeurIPS 2020 Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning AISTATS 2020