text: The paper describes a neural network-based approach to active localization based upon RGB images.  The framework employs Bayesian filtering to maintain an estimate of the agent's pose using a convolutional network model for the measurement (perception) function.  A convolutional network models the policy that governs the action of the agent.  The architecture is trained in an end-to-end manner via reinforcement learning.  The architecture is evaluated in 2D and 3D simulated environments of varying complexity and compared favorably to traditional (structured) approaches to passive and active localization. As the paper correctly points out, there is large body of work on map-based localization,  but relatively little attention has been paid to decision theoretic formulations to localization, whereby the agent's actions are chosen in order to improve localization accuracy.  More recent work instead focuses on the higher level objective of navigation, whereby any effort act in an effort to improve localization are secondary to the navigation objective.  The idea of incorporating learned representations with a structured Bayesian filtering approach is interesting,  but it's utility could be better motivated.  What are the practical benefits to learning the measurement and policy model beyond (i) the temptation to apply neural networks to this problem and (ii) the ability to learn these in an end-to-end fashion?  That's not to say that there aren't benefits, but rather that they aren't clearly demonstrated here.  Further, the paper seems to assume (as noted below) that there is no measurement uncertainty and, with the exception of the 3D evaluations, no process noise. The evaluation demonstrates that the proposed method yields estimates that are more accurate according to the proposed metric than the baseline methods, with a significant reduction in computational cost.  However, the environments considered are rather small by today's standards  and the baseline methods almost 20 years old.  Further, the evaluation makes a number of simplifying assumptions, the largest being that the measurements are not subject to noise  (the only noise that is present is in the motion for the 3D experiments).  This assumption is clearly not valid in practice.  Further, it is not clear from the evaluation whether the resulting distribution that is maintained is consistent (e.g., are the estimates over-/under-confident?).  This has important implications if the system were to actually be used on a physical system.  Further, while the computational requirements at test time are significantly lower than the baselines,  the time required for training is likely very large.  While this is less of an issue in simulation, it is important for physical deployments.  Ideally, the paper would demonstrate performance when transferring a policy trained in simulation to a physical environment (e.g., using diversification, which has proven effective at simulation-to-real transfer). Comments/Questions:* The nature of the observation space is not clear. * Recent related work has focused on learning neural policies for navigation, and any localization-specific actions are secondary to the objective of reaching the goal.  It would be interesting to discuss how one would balance the advantages of choosing actions that improve localization with those in the context of a higher-level task (or at least including a cost on actions as with the baseline method of Fox et al.). * The evaluation that assigns different textures to each wall is unrealistic. * It is not clear why the space over which the belief is maintained flips as the robot turns and shifts as it moves. * The 3D evaluation states that a 360 deg view is available.  What happens when the agent can only see in one (forward) direction? * AML includes a cost term in the objective.  Did the author(s) experiment with setting this cost to zero? * The 3D environments rely upon a particular belief size (70 x 70) being suitable for all environments.  What would happen if the test environment was larger than those encountered in training? * The comment that the PoseNet and VidLoc methods "lack a strainghtforward method to utilize past map data to do localization in a new environment" is unclear. * The environments that are considered are quite small compared to the domains currently considered for * Minor: It might be better to move Section 3 into Section 4 after introducing notation (to avoid redundancy). * The paper should be proofread for grammatical errors (e.g., "bayesian" --> "Bayesian", "gaussian" --> "Gaussian")