This paper aims to learn hierarchical policies by using a recursive policy structure regulated by a stochastic temporal grammar. The experiments show that the method is better than a flat policy for learning a simple set of block-related skills in minecraft (find, get, put, stack)  and generalizes better to a modification of the environment (size of room). The sequence of subtasks generated by the policy are interpretable.\n\n Strengths:\n- The grammar and policies are trained using a sparse reward upon task completion.  \n- The method is well ablated;  Figures 4 and 5 answered most questions I had while reading.\n - Theoretically, the method makes few assumptions about the environment and the relationships between tasks.\n - The interpretability of the final behaviors is a good result.  \n\nWeaknesses:\n- The implementation gives the agent a -0.5 reward if it generates a currently unexecutable goal g\u2019.  Providing this reward requires knowing the full state of the world.  If this hack is required, then this method would not be useful in a real world setting,  defeating the purpose of the sparse reward mentioned above.  I would really like to see how the method performs without this hack. \n - There are no comparisons to other multitask or hierarchical methods.  Progressive Networks or Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning seem like natural comparisons.\n - A video to show what the environments and tasks look like during execution would be helpful.\n - The performances of the different ablations are rather close.  Please a standard deviation over multiple training runs.  Also, why does figure 4.b not include a flat policy?\n - The stages are ordered in a semantically meaningful order (find is the first stage),  but the authors claim that the order is arbitrary.  If this claim is going to be included in the paper, it needs to be proven (results shown for random orderings)  because right now I do not believe it.  \n\nQuality:\nThe method does provide hierarchical and interpretable policies for executing instructions,  this is a meaningful direction to work on. \n\nClarity:\nAlthough the method is complicated, the paper was understandable. \n\nOriginality and significance:\nAlthough the method is interesting, I am worried that the environment has been too tailored for the method,  and that it would fail in realistic scenarios.  The results would be more significant if the tasks had an additional degree of complexity, e.g. \u201cput blue block next to the green block\u201d \u201cget the blue block in room 2\u201d.  Then the sequences of subtasks would be a bit less linear (e.g., first need to find blue, then get, then find green, then put).  At the moment the tasks are barely more than the actions provided in the environment. \n\nAnother impedance to the paper\u2019s significance is the number of hacks to make the method work  (ordering of stages, alternating policy optimization, first training each stage on only tasks of previous stage).  Because the method is only evaluated on one simple environment,  it unclear which hacks are for the method generally, and which hacks are for the method to work on the environment.