Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning. However, state-of-the-art imitation learning algorithms often assume the optimality of the given demonstration set. In this paper, we address such optimal assumption by learning only from the most suitable demonstrations in a given set. Suitability of a demonstration is estimated by whether imitating it produce desirable outcomes for achieving the goals of the tasks. For more efficient demonstration suitability assessments, the learning agent should be capable of imitating a demonstration as quick as possible, which shares similar spirit with fast adaptation in the meta-learning regime. Our framework, thus built on top of Model-Agnostic Meta-Learning, evaluates how desirable the imitated outcomes are, after adaptation to each demonstration in the set. The resulting assessments hence enable us to select suitable demonstration subsets for acquiring better imitated skills. The videos related to our experiments are available at: https://sites.google.com/view/deepdj
 The paper makes its intent plainly clear, it wants to remove the assumption that demonstrations are optimal. Thus it should show that in a case that some demonstrations are bad, it outperforms other methods which assume they are all good. The method proposed, while interesting, well-conceived and potentially novel, is not convincingly tested to this end. The paper should also show that the method can detect the bad demonstrations, and select the good demonstrations. The experiments are on toy tasks and not existing tasks in the literature. Why not use an existing dataset/domain and simply noise up the demonstrations? Furthermore, many crucial details are omitted, such as the nature of the heuristic function K, and how precisely the weighting is adapted (section 4.4). Is it done by gradient descent? We would have to know what K is, and if it is differentiable to know this. Also the writing itself needs a thorough revision. I think there may well be promise in the method, but it does not appear ready for publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning. However, state-of-the-art imitation learning algorithms often assume the optimality of the given demonstration set. In this paper, we address such optimal assumption by learning only from the most suitable demonstrations in a given set. Suitability of a demonstration is estimated by whether imitating it produce desirable outcomes for achieving the goals of the tasks. For more efficient demonstration suitability assessments, the learning agent should be capable of imitating a demonstration as quick as possible, which shares similar spirit with fast adaptation in the meta-learning regime. Our framework, thus built on top of Model-Agnostic Meta-Learning, evaluates how desirable the imitated outcomes are, after adaptation to each demonstration in the set. The resulting assessments hence enable us to select suitable demonstration subsets for acquiring better imitated skills. The videos related to our experiments are available at: https://sites.google.com/view/deepdj
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 paper makes its intent plainly clear, it wants to remove the assumption that demonstrations are optimal. Thus it should show that in a case that some demonstrations are bad, it outperforms other methods which assume they are all good. The method proposed, while interesting, well-conceived and potentially novel, is not convincingly tested to this end. The paper should also show that the method can detect the bad demonstrations, and select the good demonstrations. The experiments are on toy tasks and not existing tasks in the literature. Why not use an existing dataset/domain and simply noise up the demonstrations? Furthermore, many crucial details are omitted, such as the nature of the heuristic function K, and how precisely the weighting is adapted (section 4.4). Is it done by gradient descent? We would have to know what K is, and if it is differentiable to know this. Also the writing itself needs a thorough revision. I think there may well be promise in the method, but it does not appear ready for publication.
assistant
 The paper makes its intent plainly clear, it wants to remove the assumption that demonstrations are optimal. Thus it should show that in a case that some demonstrations are bad, it outperforms other methods which assume they are all good. The method proposed, while interesting, well-conceived and potentially novel, is not convincingly tested to this end. The paper should also show that the method can detect the bad demonstrations, and select the good demonstrations. The experiments are on toy tasks and not existing tasks in the literature. Why not use an existing dataset/domain and simply noise up the demonstrations? Furthermore, many crucial details are omitted, such as the nature of the heuristic function K, and how precisely the weighting is adapted (section 4.4). Is it done by gradient descent? We would have to know what K is, and if it is differentiable to know this. Also the writing itself needs a thorough revision. I think there may well be promise in the method, but it does not appear ready for publication.