Neural Task Graph Execution
In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and dependencies among them. Unlike existing approaches which explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships between them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural task graph solver (NTS) which encodes the task graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy that performs back-propagation over a differentiable form of the task graph to compute the influence of each subtask on the other subtasks. Our NTS is pre-trained to approximate the proposed gradient-based policy and fine-tuned through actor-critic method. The experimental results on a 2D visual domain show that our method to pre-train from the gradient-based policy significantly improves the performance of NTS. We also demonstrate that our agent can perform a complex reasoning to find the optimal way of executing the task graph and generalize well to unseen task graphs. In addition, we compare our agent with a Monte-Carlo Tree Search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of our agent can be further improved by combining with MCTS. The demo video is available at https://youtu.be/e_ZXVS5VutM.
 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
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Neural Task Graph Execution
In order to develop a scalable multi-task reinforcement learning (RL) agent that is able to execute many complex tasks, this paper introduces a new RL problem where the agent is required to execute a given task graph which describes a set of subtasks and dependencies among them. Unlike existing approaches which explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships between them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute. To solve this problem, we propose a neural task graph solver (NTS) which encodes the task graph using a recursive neural network. To overcome the difficulty of training, we propose a novel non-parametric gradient-based policy that performs back-propagation over a differentiable form of the task graph to compute the influence of each subtask on the other subtasks. Our NTS is pre-trained to approximate the proposed gradient-based policy and fine-tuned through actor-critic method. The experimental results on a 2D visual domain show that our method to pre-train from the gradient-based policy significantly improves the performance of NTS. We also demonstrate that our agent can perform a complex reasoning to find the optimal way of executing the task graph and generalize well to unseen task graphs. In addition, we compare our agent with a Monte-Carlo Tree Search (MCTS) method showing that our method is much more efficient than MCTS, and the performance of our agent can be further improved by combining with MCTS. The demo video is available at https://youtu.be/e_ZXVS5VutM.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 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
assistant
 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