The clarity of the writing could be improved substantially. Descriptions are often vague, which makes the technical details harder to understand. I think it's fine to give high-level intuitions separate from low-level details, but the current writing invites confusion. For example, at the start of Section 3, the references to buffers and clusters are vague. The text refers readers to where these concepts are described, but the high-level description doesn't really give a clear picture, making the text that follows harder to understand.
Ideas are not always presented clearly. For example:
may only exploit a small part of it, making most of the goals pointless.```
- Along the same lines, at the start of the Experiments section, when reading ```the ability of DisTop to select skills to learn``` I am left to wonder what this "ability" and "selection" refers to. This is not a criticism of word choice. The issue is that the previous section did not set up these ideas.
- Sections of the results do not seem to actually address the experimental question they are motivated by (that is, the question at the paragraph header). In general, this paper tends to draw conclusions that seem only speculatively supported by the results.
- Overall, the paper is not particularly easy to follow. The presentation lacks a clear intuition for how the pieces fit together and the experiments have little to hang on to as a result.
- The conclusions drawn from the experiments are not particularly convincing. While there is some positive validation, demonstration of the *topology* learning's success is lacking. There are some portions of the appendix that get at this, but the analysis feels incomplete. Personally, I am much more convinced by a demonstration that the underlying pieces of the algorithm are viable than by seeing that, when they are all put together, the training curves look better.
### Questions/Comments:
- The second paragraph of 2.1 is hard to follow. If the technical details are important, it may make more sense to work them into a different area of the text.
- The same applies to 2.2. The technical details are hard to follow.
- You claim "In consequence, we avoid using a hand engineered environment-specific scheduling" on page 4. Does this suggest that the $\beta$ parameter and the $\omega'$ update rate are environment independent?
- Why do DisTop and Skew-Fit have such different starting distances for Visual Pusher (Figure 1, left middle)?
- It is somewhat strange phrasing to describe Skew-Fit as having "favorite" environments (page 6).
- Overall, the paper is not particularly easy to follow. The presentation lacks a clear intuition for how the pieces fit together and the experiments have little to hang on to as a result.