Researchers have developed a new method for off-policy evaluation (OPE) in reinforcement learning when rewards are missing not at random (MNAR). This approach addresses selection bias by using future states as shadow variables to identify the full-data conditional mean reward. The proposed estimator, inspired by Fitted-Q-Evaluation, allows target policies to incorporate past missingness indicators and has demonstrated strong performance in experiments on simulated data and MIMIC-III Sepsis data. AI
IMPACT Improves the reliability of reinforcement learning models in real-world scenarios with incomplete data.
RANK_REASON Academic paper on a novel methodology in machine learning.
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