text: My problem with this paper that all the theoretical contributions / the new approach refer to 2 arXiv papers,  what's then left is an application of that approach to learning form imperfect demonstrations. Quality ====== The approach seems sound  but the paper does not provide many details on the underlying approach.  The application to learning from (partially adversarial) demonstrations is a cool idea  but effectively is a very straightforward application based on the insight that the approach can handle truly off-policy samples.  The experiments are OK  but I would have liked a more thorough analysis. Clarity ===== The paper reads well,  but it is not really clear what the claimed contribution is. Originality ========= The application seems original. Significance ========== Having an RL approach that can benefit from truly off-policy samples is highly relevant. Pros and Cons ============ + good results + interesting idea of using the algorithm for RLfD - weak experiments for an application paper - not clear what's new