Explicit Recall for Efficient Exploration
In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning. This memory records structured trajectories that have led to interesting states in the past, and can be used by the agent to revisit those states more effectively. In high-dimensional decision making problems, where deep reinforcement learning is considered crucial, our approach provides a simple, transparent and effective way that can be naturally combined with complex, deep learning models. We show how such explicit memory may be used to enhance existing exploration algorithms such as intrinsically motivated ones and count-based ones, and demonstrate our method*s advantages in various simulated environments.
 This paper is the first showing that achieving self-generated tasks during spontaneous exploration and getting reinforced by self-supervised signals is a promising way for the agent to develop skills itself. The scores are demonstrative on several tasks. It opens interesting direction for further research. REM: few typos like "An state" Please plot in dash the count methods in the graphs (use oracle information) Annexe C shall be integrated into the core of the paper. Could be simplified. The cosine metrics shall be better integrated in it.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Explicit Recall for Efficient Exploration
In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning. This memory records structured trajectories that have led to interesting states in the past, and can be used by the agent to revisit those states more effectively. In high-dimensional decision making problems, where deep reinforcement learning is considered crucial, our approach provides a simple, transparent and effective way that can be naturally combined with complex, deep learning models. We show how such explicit memory may be used to enhance existing exploration algorithms such as intrinsically motivated ones and count-based ones, and demonstrate our method*s advantages in various simulated environments.
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 is the first showing that achieving self-generated tasks during spontaneous exploration and getting reinforced by self-supervised signals is a promising way for the agent to develop skills itself. The scores are demonstrative on several tasks. It opens interesting direction for further research. REM: few typos like "An state" Please plot in dash the count methods in the graphs (use oracle information) Annexe C shall be integrated into the core of the paper. Could be simplified. The cosine metrics shall be better integrated in it.
assistant
 This paper is the first showing that achieving self-generated tasks during spontaneous exploration and getting reinforced by self-supervised signals is a promising way for the agent to develop skills itself. The scores are demonstrative on several tasks. It opens interesting direction for further research. REM: few typos like "An state" Please plot in dash the count methods in the graphs (use oracle information) Annexe C shall be integrated into the core of the paper. Could be simplified. The cosine metrics shall be better integrated in it.