Omitting-States-Irrelevant-to-Return Importance Sampling estimator for off-policy evaluation
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Updated
Jun 11, 2021 - Python
Omitting-States-Irrelevant-to-Return Importance Sampling estimator for off-policy evaluation
Implementation of "Deeply-Debiased Off-Policy Interval Estimation" (ICML, 2021) in Python
Implementation of Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings (NeurIPS, 2021) in Python
Implementation of "A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes" (ICML)
Representation Learning for OPE
Conformal Off-policy Prediction
Implementations and examples of common offline policy evaluation methods in Python.
Implementation of "A Reinforcement Learning Framework for Dynamic Mediation Analysis" (ICML 2023) in Python.
(WSDM2022 Best Paper Award Runner-Up) "Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model"
Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation
Off-Policy Interval Estimation withConfounded Markov Decision Process
(KDD2023) "Off-Policy Evaluation of Ranking Policies under Diverse User Behavior"
(NeurIPS2023) "Future-Dependent Value-Based Off-Policy Evaluation in POMDPs"
[NeurIPS 2023] Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. https://arxiv.org/abs/2310.17146
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Implementation of "Off-Policy Interval Estimation with Confounded Markov Decision Process" (JASA, 2022+)
SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection
An index of algorithms for offline reinforcement learning (offline-rl)
Reinforcement Learning Short Course
Stateful implementations of OPE algorithms, designed for use in the development of offline RL models
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