text: This is an interesting paper that builds a parameterized network to select actions for a robot in a simulated environment, with the objective of quickly reaching an internal belief state that is predictive of the true state.   This is an interesting idea  and it works much better than I would have expected.   In more careful examination it is clear that the authors have done a good job of designing a network that is partly pre-specified and partly free, in a way that makes the learning effective.   In particular- the transition model is known and fixed (in the way it is used in the belief update process) - the belief state representation is known and fixed (in the way it is used to decide whether the agent should be rewarded) - the reward function is known and fixed (as above) - the mechanics of belief update But we learn - the observation model - the control policy I'm not sure that global localization is still an open problem with known models.   Or, at least, it's not one of our worst. Early work by Cassandra, Kurien, et al used POMDP models and solvers for active localization with known transition and observation models.    It was computationally slow but effective. Similarly, although the online speed of your learned method is much better than for active Markov localization, the offline training cost is dramatically higher;   it's important to remember to be clear on this point. It is not obvious to me that it is sensible to take the cosine similarity between the feature representation of the observation and the feature representation of the state to get the entry in the likelihood map.    It would be good to make it clear this is the right measure. How is exploration done during the RL phase?   These domains are still not huge. Please explain in more detail what the memory images are doing. In general, the experiments seem to be well designed and well carried out, with several interesting extensions. I have one more major concern:  it is not the job of a localizer to arrive at a belief state with high probability mass on the true state ---it is the job of a localizer to have an accurate approximation of the true posterior under the prior and observations.    There are situations (in which, for example, the robot has gotten an unusual string of observations) in which it is correct for the robot to have more probability mass on a "wrong" state.   Or, it seems that this model may earn rewards for learning to make its beliefs overconfident.   It would be very interesting to see if you could find an objective that would actually cause the model to learn to compute the appropriate posterior. In the end, I have trouble making a recommendation: Con:  I'm not convinced that an end-to-end approach to this problem is the best one Pro: It's actually a nice idea that seems to have worked out well Con: I remain concerned that the objective is not the right one My rating would really be something like 6.5 if that were possible.