Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations. Meta-imitation learning is a promising approach towards enabling agents to learn a new task from one or a few demonstrations by leveraging experience from learning similar tasks. In the presence of task ambiguity or unobserved dynamics, demonstrations alone may not provide enough information; an agent must also try the task to successfully infer a policy. In this work, we propose a method that can learn to learn from both demonstrations and trial-and-error experience with sparse reward feedback. In comparison to meta-imitation, this approach enables the agent to effectively and efficiently improve itself autonomously beyond the demonstration data. In comparison to meta-reinforcement learning, we can scale to substantially broader distributions of tasks, as the demonstration reduces the burden of exploration. Our experiments show that our method significantly outperforms prior approaches on a set of challenging, vision-based control tasks.
The paper proposes an approach for combining meta-imitation learning and learning from trial-and-error with sparse reward feedback. The paper is well-written and the experiments are convincing. I found the idea of having separate networks for the two phases of the algorithm (instead of recollecting on-policy trial trajectories) interesting and possibly applicable in other similar settings. The paper has a comprehensive analysis of the advantages of the method over other reasonable baselines which do not have the trial-and-error element. However, in terms of the limitation of the method, the paper only has a small failure analysis section in the appendix; adding a more detailed discussion on the limitations can improve the impact of the paper further.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations. Meta-imitation learning is a promising approach towards enabling agents to learn a new task from one or a few demonstrations by leveraging experience from learning similar tasks. In the presence of task ambiguity or unobserved dynamics, demonstrations alone may not provide enough information; an agent must also try the task to successfully infer a policy. In this work, we propose a method that can learn to learn from both demonstrations and trial-and-error experience with sparse reward feedback. In comparison to meta-imitation, this approach enables the agent to effectively and efficiently improve itself autonomously beyond the demonstration data. In comparison to meta-reinforcement learning, we can scale to substantially broader distributions of tasks, as the demonstration reduces the burden of exploration. Our experiments show that our method significantly outperforms prior approaches on a set of challenging, vision-based control tasks.
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 proposes an approach for combining meta-imitation learning and learning from trial-and-error with sparse reward feedback. The paper is well-written and the experiments are convincing. I found the idea of having separate networks for the two phases of the algorithm (instead of recollecting on-policy trial trajectories) interesting and possibly applicable in other similar settings. The paper has a comprehensive analysis of the advantages of the method over other reasonable baselines which do not have the trial-and-error element. However, in terms of the limitation of the method, the paper only has a small failure analysis section in the appendix; adding a more detailed discussion on the limitations can improve the impact of the paper further.
assistant
The paper proposes an approach for combining meta-imitation learning and learning from trial-and-error with sparse reward feedback. The paper is well-written and the experiments are convincing. I found the idea of having separate networks for the two phases of the algorithm (instead of recollecting on-policy trial trajectories) interesting and possibly applicable in other similar settings. The paper has a comprehensive analysis of the advantages of the method over other reasonable baselines which do not have the trial-and-error element. However, in terms of the limitation of the method, the paper only has a small failure analysis section in the appendix; adding a more detailed discussion on the limitations can improve the impact of the paper further.