Review for NeurIPS paper: Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension

The problem of exploration in RL with function approximation is very important and any advancement on the topic is of interest for the community. The reviewers all agreed about the algorithmic and technical contribution of the paper, in particular the introduction of sensitive sampling and its analysis in the regret proof. This convinced us that the paper deserves acceptance. Nonetheless, I also encourage the authors to improve the current submission. As pointed out by R3, the assumptions used in the paper are quite strong and they may somehow limit the generality of the results.