Learning to Optimize
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
This papers adds to the literature on learning optimizers/algorithms that has gained popularity recently. The authors choose to use the framework of guided policy search at the meta-level to train the optimizers. They also opt to train on random objectives and assess transfer to a few simple tasks. As pointed below, this is a useful addition. However, the argument of using RL vs gradients at the meta-level that appears below is not clear or convincing. I urge the authors to run an experiment comparing the two approaches and to present comparative results. This is a very important question, and the scalability of this approach could very well hinge on this fact. Indeed, demonstrating both scaling to large domains and transfer to those domains is the key challenge in this domain. In summary, the idea is a good one, but the experiments are weak.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning to Optimize
Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm. We approach this problem from a reinforcement learning perspective and represent any particular optimization algorithm as a policy. We learn an optimization algorithm using guided policy search and demonstrate that the resulting algorithm outperforms existing hand-engineered algorithms in terms of convergence speed and/or the final objective value.
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>



This papers adds to the literature on learning optimizers/algorithms that has gained popularity recently. The authors choose to use the framework of guided policy search at the meta-level to train the optimizers. They also opt to train on random objectives and assess transfer to a few simple tasks. As pointed below, this is a useful addition. However, the argument of using RL vs gradients at the meta-level that appears below is not clear or convincing. I urge the authors to run an experiment comparing the two approaches and to present comparative results. This is a very important question, and the scalability of this approach could very well hinge on this fact. Indeed, demonstrating both scaling to large domains and transfer to those domains is the key challenge in this domain. In summary, the idea is a good one, but the experiments are weak.
assistant
This papers adds to the literature on learning optimizers/algorithms that has gained popularity recently. The authors choose to use the framework of guided policy search at the meta-level to train the optimizers. They also opt to train on random objectives and assess transfer to a few simple tasks. As pointed below, this is a useful addition. However, the argument of using RL vs gradients at the meta-level that appears below is not clear or convincing. I urge the authors to run an experiment comparing the two approaches and to present comparative results. This is a very important question, and the scalability of this approach could very well hinge on this fact. Indeed, demonstrating both scaling to large domains and transfer to those domains is the key challenge in this domain. In summary, the idea is a good one, but the experiments are weak.