Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders

27 Jul 2020 Andrew Bennett Nathan Kallus Lihong Li Ali Mousavi

Off-policy evaluation (OPE) in reinforcement learning is an important problem in settings where experimentation is limited, such as education and healthcare. But, in these very same settings, observed actions are often confounded by unobserved variables making OPE even more difficult... (read more)

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