Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.
The paper looks at several innovations for deep RL, and evaluates their effect on solving games in the Atari domain. The paper reads a bit like a laundry list of the researcher’s latest tricks. It is written clearly enough, but lacks a compelling message. I expect the work will be interesting to people already implementing deep RL methods, but will probably not get much attention from the broader community. The claims on p.1 suggest the approach is stable and sample efficience, and so I expected to see some theoretical analysis with respect to these properties. But this is an empirical claim; it would help to clarify that in the abstract. The proposed innovations are based on sound methods. It is particularly nice to see the same approach working for both discrete and continuous domains. The paper has reasonably complete empirical results. It would be nice to see confidence intervals on more of the plots. Also, the results don’t really tease apart the effect of each of the various innovations, so it’s harder to understand the impact of each piece and to really get intuition, for example about why ACER outperforms A3C. Also, it wasn’t clear to me why you only get matching results on discrete tasks, but get state-of-the-art on continuous tasks. The paper has good coverage of the related literature. It is nice to see this work draw more attention to Retrace, including the theoretical characterization in Sec.7.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sample Efficient Actor-Critic with Experience Replay
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.
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 looks at several innovations for deep RL, and evaluates their effect on solving games in the Atari domain. The paper reads a bit like a laundry list of the researcher’s latest tricks. It is written clearly enough, but lacks a compelling message. I expect the work will be interesting to people already implementing deep RL methods, but will probably not get much attention from the broader community. The claims on p.1 suggest the approach is stable and sample efficience, and so I expected to see some theoretical analysis with respect to these properties. But this is an empirical claim; it would help to clarify that in the abstract. The proposed innovations are based on sound methods. It is particularly nice to see the same approach working for both discrete and continuous domains. The paper has reasonably complete empirical results. It would be nice to see confidence intervals on more of the plots. Also, the results don’t really tease apart the effect of each of the various innovations, so it’s harder to understand the impact of each piece and to really get intuition, for example about why ACER outperforms A3C. Also, it wasn’t clear to me why you only get matching results on discrete tasks, but get state-of-the-art on continuous tasks. The paper has good coverage of the related literature. It is nice to see this work draw more attention to Retrace, including the theoretical characterization in Sec.7.
assistant
The paper looks at several innovations for deep RL, and evaluates their effect on solving games in the Atari domain. The paper reads a bit like a laundry list of the researcher’s latest tricks. It is written clearly enough, but lacks a compelling message. I expect the work will be interesting to people already implementing deep RL methods, but will probably not get much attention from the broader community. The claims on p.1 suggest the approach is stable and sample efficience, and so I expected to see some theoretical analysis with respect to these properties. But this is an empirical claim; it would help to clarify that in the abstract. The proposed innovations are based on sound methods. It is particularly nice to see the same approach working for both discrete and continuous domains. The paper has reasonably complete empirical results. It would be nice to see confidence intervals on more of the plots. Also, the results don’t really tease apart the effect of each of the various innovations, so it’s harder to understand the impact of each piece and to really get intuition, for example about why ACER outperforms A3C. Also, it wasn’t clear to me why you only get matching results on discrete tasks, but get state-of-the-art on continuous tasks. The paper has good coverage of the related literature. It is nice to see this work draw more attention to Retrace, including the theoretical characterization in Sec.7.