text: The paper solves the problem of how to do autonomous resets,  which is an important problem in real world RL.  The method is novel,  the explanation is clear,  and has good experimental results. Pros: 1. The approach is simple, solves a task of practical importance, and performs well in the experiments.  2. The experimental section performs good ablation studies wrt fewer reset thresholds, reset attempts, use of ensembles. Cons: 1. The method is evaluated only for 3 tasks, which are all in simulation, and on no real world tasks.  Additional tasks could be useful, especially for qualitative analysis of the learned reset policies. 2. It seems that while the method does reduce hard resets,  it would be more convincing if it can solve tasks which a model without a reset policy couldnt.  Right now, the methods without the reset policy perform about equally well on final reward. 3. The method wont be applicable to RL environments where we will need to take multiple non-invertible actions to achieve the goal (an analogy would be multiple levels in a game).  In such situations, one might want to use the reset policy to go back to intermediate “start” states from where we can continue again, rather than the original start state always. Conclusion/Significance: The approach is a step in the right direction,  and further refinements can make it a significant contribution to robotics work.