Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks
It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration. However, defining rewards that facilitate this goal within the RL paradigm remains a challenge. Here we address this problem with Siamese networks, trained to compute distances between observed behaviours and the agent’s behaviours. Given a desired motion such Siamese networks can be used to provide a reward signal to an RL agent via the distance between the desired motion and the agent’s motion. We experiment with an RNN-based comparator model that can compute distances in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we have had also found that the inclusion of multi-task data and an additional image encoding loss helps enforce the temporal consistency. These two components appear to balance reward for matching a specific instance of a behaviour versus that behaviour in general. Furthermore, we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task – the one-shot learning setting. We demonstrate our approach on humanoid agents in both 2D with 10 degrees of freedom (DoF) and 3D with 38 DoF.
This paper presents an imitation learning method that deploys previously well-studied techniques such as siamese networks, inverse RL, learning distance functions for IRL and tracking. + the paper studies an important problem of IL using visual data. + I found the ablation studies in the appendix quite useful in understanding the efficiency of the proposed method. -In terms of novelty, the proposed approach is a combination of several past works so the technical novelty is limited. Additionally, it is not clear how impactful the proposed method can be given that it is only tested on a synthetic domain which is the same as the train domain. So, from the current experimental results it is not clear if this approach would be effective to be applied in a real system (e.g. robots) on the practical side. -There are not enough evaluation done to compare with the most updated state-of-the-art baselines. The evaluations are done on just a single synthetic domain with a single character. Therefore, the train and test videos are very similar.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks
It would be desirable for a reinforcement learning (RL) based agent to learn behaviour by merely watching a demonstration. However, defining rewards that facilitate this goal within the RL paradigm remains a challenge. Here we address this problem with Siamese networks, trained to compute distances between observed behaviours and the agent’s behaviours. Given a desired motion such Siamese networks can be used to provide a reward signal to an RL agent via the distance between the desired motion and the agent’s motion. We experiment with an RNN-based comparator model that can compute distances in space and time between motion clips while training an RL policy to minimize this distance. Through experimentation, we have had also found that the inclusion of multi-task data and an additional image encoding loss helps enforce the temporal consistency. These two components appear to balance reward for matching a specific instance of a behaviour versus that behaviour in general. Furthermore, we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task – the one-shot learning setting. We demonstrate our approach on humanoid agents in both 2D with 10 degrees of freedom (DoF) and 3D with 38 DoF.
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 presents an imitation learning method that deploys previously well-studied techniques such as siamese networks, inverse RL, learning distance functions for IRL and tracking. + the paper studies an important problem of IL using visual data. + I found the ablation studies in the appendix quite useful in understanding the efficiency of the proposed method. -In terms of novelty, the proposed approach is a combination of several past works so the technical novelty is limited. Additionally, it is not clear how impactful the proposed method can be given that it is only tested on a synthetic domain which is the same as the train domain. So, from the current experimental results it is not clear if this approach would be effective to be applied in a real system (e.g. robots) on the practical side. -There are not enough evaluation done to compare with the most updated state-of-the-art baselines. The evaluations are done on just a single synthetic domain with a single character. Therefore, the train and test videos are very similar.
assistant
This paper presents an imitation learning method that deploys previously well-studied techniques such as siamese networks, inverse RL, learning distance functions for IRL and tracking. + the paper studies an important problem of IL using visual data. + I found the ablation studies in the appendix quite useful in understanding the efficiency of the proposed method. -In terms of novelty, the proposed approach is a combination of several past works so the technical novelty is limited. Additionally, it is not clear how impactful the proposed method can be given that it is only tested on a synthetic domain which is the same as the train domain. So, from the current experimental results it is not clear if this approach would be effective to be applied in a real system (e.g. robots) on the practical side. -There are not enough evaluation done to compare with the most updated state-of-the-art baselines. The evaluations are done on just a single synthetic domain with a single character. Therefore, the train and test videos are very similar.