Review for NeurIPS paper: Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning?

Weaknesses: Despite the near-optimal sample complexity bounds presented in the paper, the paper seems to fall short significantly on novelty and significance issue. Details below: Discussion on related work: The pitch of the paper is made in a way which suggests that there are no results on model-based RL when function approximation is used. However, recently, there have been many papers which look at model-based algorithms: Wen et al 2019 (which is cited in the paper) is said to be a model-based method whereas it clearly studies model-based RL. If one looks at the corresponding LQR like problems, effectively all results are model-based. Pires and Szepesvari (COLT 2016) discuss policy error bounds in model based RL.