Review for NeurIPS paper: Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Additional Feedback: Overall, the paper is quite well-written and the motivation and idea are simple and interesting. Except for the two main concerns that I will describe below, I'm mostly satisfied with the quality of this paper; thus, I'm willing to increase my score if the authors can address the following concerns. Both of these ideas have been proposed and used in several previous HRL works; thus, this can be seen as a combination of two existing ideas. This idea has been already used in [1-4], even though some of these works in different settings. For example, they predict the "distance" between current state and the sub-goal state (e.g., UVF with -1 step reward [1, 2] or success rate of (random) low-level policy [3], or k-step reachability [4]), and 2) limit the sub-goal generation to choose the near-by subgoals only (e.g., thresholding [1-4]).