Implementation of Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings (NeurIPS, 2021) in Python
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Updated
Jun 1, 2022 - Python
Implementation of Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings (NeurIPS, 2021) in Python
(NeurIPS2023) "Future-Dependent Value-Based Off-Policy Evaluation in POMDPs"
Omitting-States-Irrelevant-to-Return Importance Sampling estimator for off-policy evaluation
Conformal Off-policy Prediction
Implementation of "A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes" (ICML)
Implementation of "Off-Policy Interval Estimation with Confounded Markov Decision Process" (JASA, 2022+)
[NeurIPS 2023] Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation. https://arxiv.org/abs/2310.17146
Representation Learning for OPE
Implementation of "Deeply-Debiased Off-Policy Interval Estimation" (ICML, 2021) in Python
Stateful implementations of OPE algorithms, designed for use in the development of offline RL models
Implementation of "A Reinforcement Learning Framework for Dynamic Mediation Analysis" (ICML 2023) in Python.
(KDD2023) "Off-Policy Evaluation of Ranking Policies under Diverse User Behavior"
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
Off-Policy Interval Estimation withConfounded Markov Decision Process
(WSDM2022 Best Paper Award Runner-Up) "Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model"
SCOPE-RL: A python library for offline reinforcement learning, off-policy evaluation, and selection
Reinforcement Learning Short Course
Implementations and examples of common offline policy evaluation methods in Python.
Open Bandit Pipeline: a python library for bandit algorithms and off-policy evaluation
An index of algorithms for offline reinforcement learning (offline-rl)
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