Metacontrol for Adaptive Imagination-Based Optimization
Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this *one-size-fits-all* approach may result in the agent wasting valuable computation on easy examples, while not spending enough on hard examples. Rather than learning a single, fixed policy for solving all instances of a task, we introduce a metacontroller which learns to optimize a sequence of *imagined* internal simulations over predictive models of the world in order to construct a more informed, and more economical, solution. The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration. The models (which we call *experts*) can be state transition models, action-value functions, or any other mechanism that provides information useful for solving the task, and can be learned on-policy or off-policy in parallel with the metacontroller. When the metacontroller, controller, and experts were trained with *interaction networks* (Battaglia et al., 2016) as expert models, our approach was able to solve a challenging decision-making problem under complex non-linear dynamics. The metacontroller learned to adapt the amount of computation it performed to the difficulty of the task, and learned how to choose which experts to consult by factoring in both their reliability and individual computational resource costs. This allowed the metacontroller to achieve a lower overall cost (task loss plus computational cost) than more traditional fixed policy approaches. These results demonstrate that our approach is a powerful framework for using rich forward models for efficient model-based reinforcement learning.
A well written paper and an interesting construction - I thoroughly enjoyed reading it. I found the formalism a bit hard to follow without specific examples- that is, it wasn*t clear to me at first what the specific components in figure 1A were. What constitutes the controller, a control, the optimizer, what was being optimized, etc., in specific cases. Algorithm boxes may have been helpful, especially in the case of your experiments. A description of existing models that fall under your conceptual framework might help as well. In Practical Bayesian Optimization of Machine Learning Algorithms, Snoek, Larochelle and Adams propose optimizing with respect to expected improvement per second to balance computation cost and performance loss. It might be interesting to see how this falls into your framework. Experimental results were presented clearly and well illustrated the usefulness of the metacontroller. I*m curious to see the results of using more metaexperts.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Metacontrol for Adaptive Imagination-Based Optimization
Many machine learning systems are built to solve the hardest examples of a particular task, which often makes them large and expensive to run---especially with respect to the easier examples, which might require much less computation. For an agent with a limited computational budget, this *one-size-fits-all* approach may result in the agent wasting valuable computation on easy examples, while not spending enough on hard examples. Rather than learning a single, fixed policy for solving all instances of a task, we introduce a metacontroller which learns to optimize a sequence of *imagined* internal simulations over predictive models of the world in order to construct a more informed, and more economical, solution. The metacontroller component is a model-free reinforcement learning agent, which decides both how many iterations of the optimization procedure to run, as well as which model to consult on each iteration. The models (which we call *experts*) can be state transition models, action-value functions, or any other mechanism that provides information useful for solving the task, and can be learned on-policy or off-policy in parallel with the metacontroller. When the metacontroller, controller, and experts were trained with *interaction networks* (Battaglia et al., 2016) as expert models, our approach was able to solve a challenging decision-making problem under complex non-linear dynamics. The metacontroller learned to adapt the amount of computation it performed to the difficulty of the task, and learned how to choose which experts to consult by factoring in both their reliability and individual computational resource costs. This allowed the metacontroller to achieve a lower overall cost (task loss plus computational cost) than more traditional fixed policy approaches. These results demonstrate that our approach is a powerful framework for using rich forward models for efficient model-based reinforcement learning.
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>



A well written paper and an interesting construction - I thoroughly enjoyed reading it. I found the formalism a bit hard to follow without specific examples- that is, it wasn*t clear to me at first what the specific components in figure 1A were. What constitutes the controller, a control, the optimizer, what was being optimized, etc., in specific cases. Algorithm boxes may have been helpful, especially in the case of your experiments. A description of existing models that fall under your conceptual framework might help as well. In Practical Bayesian Optimization of Machine Learning Algorithms, Snoek, Larochelle and Adams propose optimizing with respect to expected improvement per second to balance computation cost and performance loss. It might be interesting to see how this falls into your framework. Experimental results were presented clearly and well illustrated the usefulness of the metacontroller. I*m curious to see the results of using more metaexperts.
assistant
A well written paper and an interesting construction - I thoroughly enjoyed reading it. I found the formalism a bit hard to follow without specific examples- that is, it wasn*t clear to me at first what the specific components in figure 1A were. What constitutes the controller, a control, the optimizer, what was being optimized, etc., in specific cases. Algorithm boxes may have been helpful, especially in the case of your experiments. A description of existing models that fall under your conceptual framework might help as well. In Practical Bayesian Optimization of Machine Learning Algorithms, Snoek, Larochelle and Adams propose optimizing with respect to expected improvement per second to balance computation cost and performance loss. It might be interesting to see how this falls into your framework. Experimental results were presented clearly and well illustrated the usefulness of the metacontroller. I*m curious to see the results of using more metaexperts.