Ying Zhou

Learning from the Unseen: Offline Reinforcement Learning with Hidden Actions

This work leverages the next-state variable as a natural proxy for the unobserved action and proposes an influence-function-based estimator called LURE (Learning from the Unseen: Robust Estimator), which is multiply robust, remaining consistent under several combinations of correctly specified nuisance components, and...