By maximizing an information theoretic objective, a few recent methods empower the agent to explore the environment and learn skills without extrinsic reward. However, when considering to use multiple consecutive skills to complete a specific task, the transition from one to another cannot guarantee the success of the process due to the evident gap between skills. In this paper, we propose to learn transitional skills (LTS) in addition to creating diverse primitive skills. By introducing an extra latent variable for transitional skills and a compensation term for intrinsic reward, our LTS method discovers both primitive and transitional skills by maximizing the mutual information with compensation. By considering various robotic tasks, our results demonstrate the effectiveness of LTS on learning both diverse primitive skills and transitional skills, and show its superiority in smooth transition of skills over the state-of-the-art baseline DIAYN. In addition, we further show the significance of transitional skills for learning downstream tasks.
What is the specific question/problem tackled by the paper?
This paper addresses the problem of learning and using useful skills through unsupervised RL. While past work (VIC, DIAYN) found skills by maximizing the mutual information between skills and states, this paper (LTS) aims to find transitional skills to move between the primitive skills.
The proposed method is, if the primitive skills found by DIAYN are labeled w_i, to find skills z_[i,j,k] which corresponds to the kth state while transitioning from w_i to w_j. These transition skills are learned jointly with the primitive skills using the mutual information objective.
The results in the paper are on mountain car, cart pole, and pendulum. The plots show that distinct primitive skills can be learned in these environments, and that the transition skill transitions between them. The results do not show the actual performance of using these skills for any specific task, which was the motivation presented in the introduction. The 
Is the approach well motivated?
The approach is not well motivated. The paper claims that transitional skills are necessary, but does not show any evidence of an agents behavior improving by using transitional skills. 
I argue to reject the paper in its current state; Sections 3 and 4 need to be rewritten to be more easily understandable, and the need for transitional skills should be more empirically motivated.
Clarity Issues:
- The method is presented in a very confusing way. I am not confident that I understand how the transitional skills are defined. 
- Figure 2: What is the “statistical characteristic” and “feature value”? The vague terms are not defined. Which value? What statistic?
- Figure 5: I see that LTS transitions more smoothly between skills, but why is DIAYN’s fast transition bad? Clearly it still succeeds at reaching the other state.
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Update
Due to the additional experiments that show the benefit of LTS, I have increased my score to weak reject.
Section 6.5: Thank you for adding this additional experiment. This section lacks details about how the experiment was run. How was the meta-policy trained? In “3) LTS-master: Transfer policy with optimal weights learned by meta-policy”, why is there transfer? Transfer from what to what? 
“Moreover, we conduct hierarchical framework to weight (or choose) the action modeled by different primitive skills.” What does this sentence mean?
Reading over the paper again, the paper needs several editing passes to fix the grammar, as a number of sections (like above) are not understandable. While the paper has improved and is on a good track, I do not think it is yet ready for publication.