Provably Efficient Interaction-Grounded Learning with Personalized Reward

Interaction-Grounded Learning (IGL) [Xie et al., 2021] is a powerful framework in To deal with personalized rewards that are ubiquitous in applications such as recommendation systems, Maghakian et al. [2022] study a version of IGL with context-dependent feedback, but their algorithm does not come with theoretical guarantees. Building on this estimator, we propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting.