Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact yet reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.
 Review: This paper proposed "Dopamine", a new framework for DeepRL. While this framework seems to be useful and the paper seems like a useful guide for using the framework, I didn*t think that the paper had enough scientific novelty to be an ICLR paper. I think that papers on novel frameworks can be suitable, but they should demonstrate that they*re able to do something or provide a novel capability which has not been demonstrated before. Strengths: -Having a standardized tool for keeping replay buffers seems useful. -The Dopamine framework is written in Python and has 12 files, which means that it should be reasonably easy for users to understand how it*s functioning and change things or debug. -The paper has a little bit of analysis of how different settings effect results (such as how to terminate episodes) but I*m not sure that it does much to help us in understanding the framework. I suppose it*s useful to understand that the settings which are configurable in the framework affect results? -The result on how sticky actions affect results is nice but I*m not sure what it adds over the Machado (2018) discussion. Weaknesses: -Given that the paper is about documenting a new framework, it would have been nice to see more comprehensive baselines documented for different methods and settings. -I don*t understand the point of 2.1, in that it seems somewhat trivial that research has been done on different architectures and algorithms. -In section 4.2, I wonder if the impact of training mode vs. evaluation mode would be larger if the model used a stochastic regularizer. I suspect that in general changing to evaluation mode could have a significant impact.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Dopamine: A Research Framework for Deep Reinforcement Learning
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new research framework for deep RL that aims to support some of that diversity. Dopamine is open-source, TensorFlow-based, and provides compact yet reliable implementations of some state-of-the-art deep RL agents. We complement this offering with a taxonomy of the different research objectives in deep RL research. While by no means exhaustive, our analysis highlights the heterogeneity of research in the field, and the value of frameworks such as ours.
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>



 Review: This paper proposed "Dopamine", a new framework for DeepRL. While this framework seems to be useful and the paper seems like a useful guide for using the framework, I didn*t think that the paper had enough scientific novelty to be an ICLR paper. I think that papers on novel frameworks can be suitable, but they should demonstrate that they*re able to do something or provide a novel capability which has not been demonstrated before. Strengths: -Having a standardized tool for keeping replay buffers seems useful. -The Dopamine framework is written in Python and has 12 files, which means that it should be reasonably easy for users to understand how it*s functioning and change things or debug. -The paper has a little bit of analysis of how different settings effect results (such as how to terminate episodes) but I*m not sure that it does much to help us in understanding the framework. I suppose it*s useful to understand that the settings which are configurable in the framework affect results? -The result on how sticky actions affect results is nice but I*m not sure what it adds over the Machado (2018) discussion. Weaknesses: -Given that the paper is about documenting a new framework, it would have been nice to see more comprehensive baselines documented for different methods and settings. -I don*t understand the point of 2.1, in that it seems somewhat trivial that research has been done on different architectures and algorithms. -In section 4.2, I wonder if the impact of training mode vs. evaluation mode would be larger if the model used a stochastic regularizer. I suspect that in general changing to evaluation mode could have a significant impact.
assistant
 Review: This paper proposed "Dopamine", a new framework for DeepRL. While this framework seems to be useful and the paper seems like a useful guide for using the framework, I didn*t think that the paper had enough scientific novelty to be an ICLR paper. I think that papers on novel frameworks can be suitable, but they should demonstrate that they*re able to do something or provide a novel capability which has not been demonstrated before. Strengths: -Having a standardized tool for keeping replay buffers seems useful. -The Dopamine framework is written in Python and has 12 files, which means that it should be reasonably easy for users to understand how it*s functioning and change things or debug. -The paper has a little bit of analysis of how different settings effect results (such as how to terminate episodes) but I*m not sure that it does much to help us in understanding the framework. I suppose it*s useful to understand that the settings which are configurable in the framework affect results? -The result on how sticky actions affect results is nice but I*m not sure what it adds over the Machado (2018) discussion. Weaknesses: -Given that the paper is about documenting a new framework, it would have been nice to see more comprehensive baselines documented for different methods and settings. -I don*t understand the point of 2.1, in that it seems somewhat trivial that research has been done on different architectures and algorithms. -In section 4.2, I wonder if the impact of training mode vs. evaluation mode would be larger if the model used a stochastic regularizer. I suspect that in general changing to evaluation mode could have a significant impact.