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.
This paper introduces an approach to reinforcement learning and control wherein, rather than training a single controller to perform a task, a metacontroller with access to a base-level controller and a number of accessory « experts » is utilized. The job of the metacontroller is to decide how many times to call the controller and the experts, and which expert to invoke at which iteration. (The controller is a bit special in that in addition to being provided the current state, it is given a summary of the history of previous calls to itself and previous experts.) The sequence of controls and expert advice is embedded into a fixed-size vector through an LSTM. The method is tested on an N-body control task, where it is shown that there are benefits to multiple iterations (« pondering ») even for simple experts, and that the metacontroller can deliver accuracy and computational cost benefits over fixed-iteration controls. The paper is in general well written, and reasonably easy to follow. As the authors note, the topic of metareasoning has been studied to some extent in AI, but its use as a differentiable and fully trainable component within an RL system appears new. At this stage, it is difficult to evaluate the impact of this kind of approach: the overall model architecture is intriguing and probably merits publication, but whether and how this will scale to other domains remains the subject of future work. The experimental validation is interesting and well carried out, but remains of limited scope. Moreover, given such a complex architecture, there should be a discussion of the training difficulties and convergence issues, if any. Here are a few specific comments, questions and suggestions: 1) in Figure 1A, the meaning of the graphical language should be explained. For instance, there are arrows of different thickness and line style — do these mean different things? 2) in Figure 3, the caption should better explain the contents of the figure. For example, what do the colours of the different lines refer to? Also, in the top row, there are dots and error bars that are given, but this is explained only in the « bottom row » part. This makes understanding this figure difficult. 3) in Figure 4, the shaded area represents a 95% confidence interval on the regression line; in addition, it would be helpful to give a standard error on the regression slope (to verify that it excludes zero, i.e. the slope is significant), as well as a fraction of explained variance (R^2). 4) in Figure 5, the fraction of samples using the MLP expert does not appear to decrease monotonically with the increasing cost of the MLP expert (i.e. the bottom left part of the right plot, with a few red-shaded boxes). Why is that? Is there lots of variance in these fractions from experiment to experiment? 5) the supplementary materials are very helpful. Thank you for all these details.
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>



This paper introduces an approach to reinforcement learning and control wherein, rather than training a single controller to perform a task, a metacontroller with access to a base-level controller and a number of accessory « experts » is utilized. The job of the metacontroller is to decide how many times to call the controller and the experts, and which expert to invoke at which iteration. (The controller is a bit special in that in addition to being provided the current state, it is given a summary of the history of previous calls to itself and previous experts.) The sequence of controls and expert advice is embedded into a fixed-size vector through an LSTM. The method is tested on an N-body control task, where it is shown that there are benefits to multiple iterations (« pondering ») even for simple experts, and that the metacontroller can deliver accuracy and computational cost benefits over fixed-iteration controls. The paper is in general well written, and reasonably easy to follow. As the authors note, the topic of metareasoning has been studied to some extent in AI, but its use as a differentiable and fully trainable component within an RL system appears new. At this stage, it is difficult to evaluate the impact of this kind of approach: the overall model architecture is intriguing and probably merits publication, but whether and how this will scale to other domains remains the subject of future work. The experimental validation is interesting and well carried out, but remains of limited scope. Moreover, given such a complex architecture, there should be a discussion of the training difficulties and convergence issues, if any. Here are a few specific comments, questions and suggestions: 1) in Figure 1A, the meaning of the graphical language should be explained. For instance, there are arrows of different thickness and line style — do these mean different things? 2) in Figure 3, the caption should better explain the contents of the figure. For example, what do the colours of the different lines refer to? Also, in the top row, there are dots and error bars that are given, but this is explained only in the « bottom row » part. This makes understanding this figure difficult. 3) in Figure 4, the shaded area represents a 95% confidence interval on the regression line; in addition, it would be helpful to give a standard error on the regression slope (to verify that it excludes zero, i.e. the slope is significant), as well as a fraction of explained variance (R^2). 4) in Figure 5, the fraction of samples using the MLP expert does not appear to decrease monotonically with the increasing cost of the MLP expert (i.e. the bottom left part of the right plot, with a few red-shaded boxes). Why is that? Is there lots of variance in these fractions from experiment to experiment? 5) the supplementary materials are very helpful. Thank you for all these details.
assistant
This paper introduces an approach to reinforcement learning and control wherein, rather than training a single controller to perform a task, a metacontroller with access to a base-level controller and a number of accessory « experts » is utilized. The job of the metacontroller is to decide how many times to call the controller and the experts, and which expert to invoke at which iteration. (The controller is a bit special in that in addition to being provided the current state, it is given a summary of the history of previous calls to itself and previous experts.) The sequence of controls and expert advice is embedded into a fixed-size vector through an LSTM. The method is tested on an N-body control task, where it is shown that there are benefits to multiple iterations (« pondering ») even for simple experts, and that the metacontroller can deliver accuracy and computational cost benefits over fixed-iteration controls. The paper is in general well written, and reasonably easy to follow. As the authors note, the topic of metareasoning has been studied to some extent in AI, but its use as a differentiable and fully trainable component within an RL system appears new. At this stage, it is difficult to evaluate the impact of this kind of approach: the overall model architecture is intriguing and probably merits publication, but whether and how this will scale to other domains remains the subject of future work. The experimental validation is interesting and well carried out, but remains of limited scope. Moreover, given such a complex architecture, there should be a discussion of the training difficulties and convergence issues, if any. Here are a few specific comments, questions and suggestions: 1) in Figure 1A, the meaning of the graphical language should be explained. For instance, there are arrows of different thickness and line style — do these mean different things? 2) in Figure 3, the caption should better explain the contents of the figure. For example, what do the colours of the different lines refer to? Also, in the top row, there are dots and error bars that are given, but this is explained only in the « bottom row » part. This makes understanding this figure difficult. 3) in Figure 4, the shaded area represents a 95% confidence interval on the regression line; in addition, it would be helpful to give a standard error on the regression slope (to verify that it excludes zero, i.e. the slope is significant), as well as a fraction of explained variance (R^2). 4) in Figure 5, the fraction of samples using the MLP expert does not appear to decrease monotonically with the increasing cost of the MLP expert (i.e. the bottom left part of the right plot, with a few red-shaded boxes). Why is that? Is there lots of variance in these fractions from experiment to experiment? 5) the supplementary materials are very helpful. Thank you for all these details.