text: In the context of multitask reinforcement learning, this paper considers the problem of learning behaviours when given specifications of subtasks and the relationship between them, in the form of a task graph. The paper presents a neural task graph solver (NTS), which encodes this as a recursive-reverse-recursive neural network. A method for learning this is presented, and fine tuned with an actor-critic method. The approach is evaluated in a multitask grid world domain. This paper addresses an important issue in scaling up reinforcement learning to large domains with complex interdependencies in subtasks. The method is novel, and the paper is generally well written. I unfortunately have several issues with the paper in its current form, most importantly around the experimental comparisons. The paper is severely weakened by not comparing experimentally to other learning (hierarchical) schemes, such as options or HAMs. None of the comparisons in the paper feature any learning. Ideally, one should see the effect of learning with options (and not primitive actions) to fairly compare against the proposed framework. At some level, I question whether the proposed framework is doing any more than just value function propagation at a task level, and these experiments would help resolve this. Additionally, the example domain makes no sense. Rather use something more standard, with well-known baselines, such as the taxi domain. I would have liked to see a discussion in the related work comparing the proposed approach to the long history of reasoning with subtasks from the classical planning literature, notably HTNs. I found the description of the training of the method to be rather superficial, and I don't think it could be replicated from the paper in its current level of detail. The approach raises the natural questions of where the tasks and the task graphs come from. Some acknowledgement and discussion of this would be useful. The legend in the middle of Fig 4 obscures the plot (admittedly not substantially). There are also a number of grammatical errors in the paper, including the following non-exhaustive list: 2: as well as how to do -> as well as how to do it Fig 2 caption: through bottom-up -> through a bottom-up 3: Let S be a set of state -> Let S be a set of states 3: form of task graph -> form of a task graph 3: In addtion -> In addition 4: which is propagates -> which propagates 5: investigated following -> investigated the following