Paper:
Research on exploration in reinforcement learning, as applied to Atari 2600 gameplaying, has emphasized tackling difficult exploration problems such as MON-TEZUMA'S REVENGE (Bellemare et al., 2016). Recently, bonus-based exploration methods, which explore by augmenting the environment reward, have reached above-human average performance on such domains. In this paper we reassess popular bonus-based exploration methods within a common evaluation framework. We combine Rainbow (Hessel et al., 2018) with different exploration bonuses and evaluate its performance on MONTEZUMA'S REVENGE, Bellemare et al.'s set of hard of exploration games with sparse rewards, and the whole Atari 2600 suite. We find that while exploration bonuses lead to higher score on MONTEZUMA'S REVENGE they do not provide meaningful gains over the simpler -greedy scheme. In fact, we find that methods that perform best on that game often underperform -greedy on easy exploration Atari 2600 games. We find that our conclusions remain valid even when hyperparameters are tuned for these easy-exploration games. Finally, we find that none of the methods surveyed benefit from additional training samples (1 billion frames, versus Rainbow's 200 million) on Bellemare et al.'s hard exploration games. Our results suggest that recent gains in MONTEZUMA'S REVENGE may be better attributed to architecture change, rather than better exploration schemes; and that the real pace of progress in exploration research for Atari 2600 games may have been obfuscated by good results on a single domain.

Review:
Updated review: I am overall happy with the response of the authors. I can appreciate the contributions of the paper and I am happy to recommend accept. The empirical study offers some insights into deep RL methods for ATARI games and raises some key questions. I feel the current version of the paper does not build upon these insights to propose a new method. 
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Summary: This paper presents a detailed empirical study of the recent bonus based exploration method on the Atari game suite. The paper concludes that methods that perform well on Montezuma’s revenge do not necessarily perform well on the other games, sometimes, even worse than the eps-greedy approach. This also leads to the conclusion that recent results on the game Montezuma’s revenge can be attributed to architectural changes instead of the exploration method. 
I think this is a-ok paper in that it does what it says it does. The paper is clear and well-written. 
I think the main contribution of the paper is that it raises some questions over existing methods/trends in solving exploration problems in reinforcement learning by comparing the performance of multiple methods across various games in ATARI suite. 
I think this is relevant to the ICLR community and will be appreciated by it. 
However, I also feel that while the paper runs a satisfactory empirical analysis, it was all too much focussed on the existing methods. Throughout the paper, the experiments and results raise questions on the robustness and generalization of existing exploration methods across various ATARI games, but the paper puts absolutely zero effort into investigating if there is a quick fix to the questions it poses. For example, one could easily investigate in the CTS method if the factor by which exploration bonus dies N^{alpha} (alpha=-1/2 by default) changes, then does it do better or worse (more below on this).
I can understand that might not be the aim of the paper but still. 
Here are a couple of points that I felt conflicted/confused about the paper: 
- The conclusion of the paper is that ‘progress of exploration in ATARI suite is obfuscated by good results in single domain’. I am confused if the paper is making a narrow point that (1) dont focus on Montezuma’s revenge OR (2) is it admitting a broader point that focussing on even ATARI is probably not a good choice. I am not saying that I know the answer to this question, but I am unclear as to what is the question the paper is trying to raise. If it is saying (1st) then I find it contradictory that it is not ok to focus on MR but it is ok to focus on ATARI as a single domain; if it is saying the second then also it is contradictory because the paper only experiments with the ATARI suite.
 
- It is interesting to note that noisy networks are most robust to hyperparameter optimization on a separate set of games when tested on a different set of games. It is also interesting to note that noisy networks are the only exploration bonus method that does not decrease/reduce exploration as the experience of the agent increases. I would have liked to see if the paper had made an attempt to investigate this. I feel such a hypothesis would have been easy to investigate with simple modifications to the CTS methods. Currently, the exploration bonus goes down by the factor of 1/sqrt(N)  in the CTS method. A comparison that showed the performance of CTS for a couple more values of factors such as (1/N) or (1/N)^{1/4} would have been nice to see if that mattered.
- One of the comparisons I did not particularly find fair was when the hyperparameters of various methods were tuned to play MR and then the hyperparameters were fixed and the method were tested on other ATARI games. 
- Another point I felt was missing was checking if rainbow DQN is really the reason behind the observed performance of the methods. It would have been interesting to know how the methods performed when combined with the original DQN algorithm.