Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.
 This paper proposes a distributed architecture for deep reinforcement learning at scale, specifically, focusing on adding parallelization in actor algorithm in Prioritized Experience Replay framework. It has a very nice introduction and literature review of Prioritized experience replay and also suggested to parallelize the actor algorithm by simply adding more actors to execute in parallel, so that the experience replay can obtain more data for the learner to sample and learn. Not surprisingly, as this framework is able to learn from way more data (e.g. in Atari), it outperforms the baselines, and Figure 4 clearly shows the more actors we have the better performance we will have. While the strength of this paper is clearly the good writing as well as rigorous experimentation, the main concern I have with this paper is novelty. It is in my opinion a somewhat trivial extension of the previous work of Prioritized experience replay in literature; hence the challenge of the work is not quite clear. Hence, I feel adding some practical learnings of setting up such infrastructure might add more flavor to this paper, for example.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Distributed Prioritized Experience Replay
We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact with their own instances of the environment by selecting actions according to a shared neural network, and accumulate the resulting experience in a shared experience replay memory; the learner replays samples of experience and updates the neural network. The architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time.
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 proposes a distributed architecture for deep reinforcement learning at scale, specifically, focusing on adding parallelization in actor algorithm in Prioritized Experience Replay framework. It has a very nice introduction and literature review of Prioritized experience replay and also suggested to parallelize the actor algorithm by simply adding more actors to execute in parallel, so that the experience replay can obtain more data for the learner to sample and learn. Not surprisingly, as this framework is able to learn from way more data (e.g. in Atari), it outperforms the baselines, and Figure 4 clearly shows the more actors we have the better performance we will have. While the strength of this paper is clearly the good writing as well as rigorous experimentation, the main concern I have with this paper is novelty. It is in my opinion a somewhat trivial extension of the previous work of Prioritized experience replay in literature; hence the challenge of the work is not quite clear. Hence, I feel adding some practical learnings of setting up such infrastructure might add more flavor to this paper, for example.
assistant
 This paper proposes a distributed architecture for deep reinforcement learning at scale, specifically, focusing on adding parallelization in actor algorithm in Prioritized Experience Replay framework. It has a very nice introduction and literature review of Prioritized experience replay and also suggested to parallelize the actor algorithm by simply adding more actors to execute in parallel, so that the experience replay can obtain more data for the learner to sample and learn. Not surprisingly, as this framework is able to learn from way more data (e.g. in Atari), it outperforms the baselines, and Figure 4 clearly shows the more actors we have the better performance we will have. While the strength of this paper is clearly the good writing as well as rigorous experimentation, the main concern I have with this paper is novelty. It is in my opinion a somewhat trivial extension of the previous work of Prioritized experience replay in literature; hence the challenge of the work is not quite clear. Hence, I feel adding some practical learnings of setting up such infrastructure might add more flavor to this paper, for example.