Review for NeurIPS paper: Linear Disentangled Representations and Unsupervised Action Estimation

Relation to Prior Work: The second part of this work which aims at recovering the action space of an interactive agent is reminiscent of several prior works [1-5]. Although in this work the actions taken are unknown, the rewards used to recover which actions were taken is similar to ones use in some of these works [1,3,4] to reward disentangled feature/policy pairs. It may be interesting to compare to/consider them, especially considering that the proposed method seems to have the exact same weaknesses; an almost perfect disentanglement in simple environments such as gridworlds, the occasional suboptimal minima where learning gets stuck with redundant or mis-disentangled actions/policies, and an inability to deal with longer action sequences correctly. An interesting, if unsatisfactory conclusion from these works is that such approaches do not cleanly scale to more complex observation and action spaces. I wonder if the same is true here.