Unsupervised Meta-Learning for Reinforcement Learning
Meta-learning is a powerful tool that learns how to quickly adapt a model to new tasks. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks. The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning. We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods. Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.
 The authors propose a framework for unsupervised meta-reinforcement learning. This aims to perform meta-learning in reinforcement learning context without an specification of the meta tasks being pre-specified. The authors propose two algorithms to acquire the task distributions (unsupervised). In particular the better performing approach relies on the recently introduced DIAYN algorithm. Experiments are presented on several simple benchmark datasets. The authors propose an interesting formulation of a useful problem: finding tasks automatically that aid meta-learning. To the best of my knowledge this is indeed a novel idea and indeed an important one. On the other hand the authors only take relatively early steps towards solving this task and the discussion of what is a good unsupervised task selection is underwhelming. Indeed one is not left of a clear idea of what kind of inductive biases would be a valid approach to this problem and why the authors consider specifically the two approaches described. For the experiments it seems a lot of the key improvements come from the DIAYN algorithm. The experiments are also presented on relatively toy tasks and mainly compare to RL from scratch approaches. It would be interesting to see the effectiveness of these methods on harder problems. For the experiments I would be interested to know if one could compare directly to using DIAYN as in the original Eysenbach et al for example as an initialization. Overall the paper presents several interesting results and I think the high level formulation could have some potential impacts, although the limits of such an approach are not completely clear and whether it can be effective on complex tasks is not fully known yet.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Unsupervised Meta-Learning for Reinforcement Learning
Meta-learning is a powerful tool that learns how to quickly adapt a model to new tasks. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by meta-learning prior tasks. The performance of meta-learning algorithms critically depends on the tasks available for meta-training: in the same way that supervised learning algorithms generalize best to test points drawn from the same distribution as the training points, meta-learning methods generalize best to tasks from the same distribution as the meta-training tasks. In effect, meta-reinforcement learning offloads the design burden from algorithm design to task design. If we can automate the process of task design as well, we can devise a meta-learning algorithm that is truly automated. In this work, we take a step in this direction, proposing a family of unsupervised meta-learning algorithms for reinforcement learning. We describe a general recipe for unsupervised meta-reinforcement learning, and describe an effective instantiation of this approach based on a recently proposed unsupervised exploration technique and model-agnostic meta-learning. We also discuss practical and conceptual considerations for developing unsupervised meta-learning methods. Our experimental results demonstrate that unsupervised meta-reinforcement learning effectively acquires accelerated reinforcement learning procedures without the need for manual task design, significantly exceeds the performance of learning from scratch, and even matches performance of meta-learning methods that use hand-specified task distributions.
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>



 The authors propose a framework for unsupervised meta-reinforcement learning. This aims to perform meta-learning in reinforcement learning context without an specification of the meta tasks being pre-specified. The authors propose two algorithms to acquire the task distributions (unsupervised). In particular the better performing approach relies on the recently introduced DIAYN algorithm. Experiments are presented on several simple benchmark datasets. The authors propose an interesting formulation of a useful problem: finding tasks automatically that aid meta-learning. To the best of my knowledge this is indeed a novel idea and indeed an important one. On the other hand the authors only take relatively early steps towards solving this task and the discussion of what is a good unsupervised task selection is underwhelming. Indeed one is not left of a clear idea of what kind of inductive biases would be a valid approach to this problem and why the authors consider specifically the two approaches described. For the experiments it seems a lot of the key improvements come from the DIAYN algorithm. The experiments are also presented on relatively toy tasks and mainly compare to RL from scratch approaches. It would be interesting to see the effectiveness of these methods on harder problems. For the experiments I would be interested to know if one could compare directly to using DIAYN as in the original Eysenbach et al for example as an initialization. Overall the paper presents several interesting results and I think the high level formulation could have some potential impacts, although the limits of such an approach are not completely clear and whether it can be effective on complex tasks is not fully known yet.
assistant
 The authors propose a framework for unsupervised meta-reinforcement learning. This aims to perform meta-learning in reinforcement learning context without an specification of the meta tasks being pre-specified. The authors propose two algorithms to acquire the task distributions (unsupervised). In particular the better performing approach relies on the recently introduced DIAYN algorithm. Experiments are presented on several simple benchmark datasets. The authors propose an interesting formulation of a useful problem: finding tasks automatically that aid meta-learning. To the best of my knowledge this is indeed a novel idea and indeed an important one. On the other hand the authors only take relatively early steps towards solving this task and the discussion of what is a good unsupervised task selection is underwhelming. Indeed one is not left of a clear idea of what kind of inductive biases would be a valid approach to this problem and why the authors consider specifically the two approaches described. For the experiments it seems a lot of the key improvements come from the DIAYN algorithm. The experiments are also presented on relatively toy tasks and mainly compare to RL from scratch approaches. It would be interesting to see the effectiveness of these methods on harder problems. For the experiments I would be interested to know if one could compare directly to using DIAYN as in the original Eysenbach et al for example as an initialization. Overall the paper presents several interesting results and I think the high level formulation could have some potential impacts, although the limits of such an approach are not completely clear and whether it can be effective on complex tasks is not fully known yet.