Review for NeurIPS paper: A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs

Correctness: The main technical content seems to be correct. I have the following questions though: When using the linear assumption for the reward and the dynamics, the feature selection/setting is crutial. To relax the linear assumption, it is also mentioned, features can be pre-trained. What would be the recommended way to pre-learn it? For possible violation of the assumptions, how it would affect the results in practice?