Paper:
Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments. Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization. We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.

Review:
This paper proposes methods to improve generalization in deep reinforcement learning with an emphasis on unseen environments. The main contribution is essentially a data augmentation technique that perturbs the input observations using a noise generated from the range space of a random convolutional network. The empirical results look impressive and demonstrate the effectiveness of the method. The experiments are thorough (includes even adversarial attack) and the core method is novel as far as I am aware.
That said, I have a couple of concerns regarding this paper and I would be willing to change my score if authors can address these.
1) Feature matching loss (Eq 2) is presented as a novel contribution without referring to related work in semisupervised learning literature. This is essentially consistency training. See:
a) Miyato, Takeru, et al. "Virtual adversarial training: a regularization method for supervised and semi-supervised learning." IEEE transactions on pattern analysis and machine intelligence 41.8 (2018): 1979-1993.
b) Xie, Qizhe, et al. "Unsupervised data augmentation." arXiv preprint arXiv:1904.12848 (2019).
2) The main contribution appears to be a data augmentation technique where we add a random neural net based perturbation to the state. My question is:
*Why don't you first evaluate this on computer vision tasks given that the core idea is data augmentation for images?*
If this technique is so powerful, shouldn't this do a great job in CIFAR10, Imagenet etc? Instead authors only provide a niche example (bright vs dark cat/dogs).
If this can compete with top augmentation techniques on Imagenet (e.g. autoagument), then it can explain the RL performance. Otherwise, please provide some intuition on why this works so well on RL but not as well on computer vision tasks. Is it the unseen environment diversity of RL challenges?
3) While proposed method performs well on the benchmarks, it is not clear whether authors compare to the state-of-the-art algorithms. For each task (CoinRun, DeepMind Lab, etc), please explicitly state the best prior result (e.g. Espeholt et al, Tobin et al, Cobbe et al etc) so that proposed method's performance can be better assessed.
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After rebuttal: Authors addressed most of my comments. I also found the new experimental results (Fig 5 and 7) very insightful. I increase my score to Weak Accept.
For future improvement: More realistic experiments on computer vision tasks (besides cats and dogs) would be welcome. Otherwise, please justify why proposed strategy is particularly good for RL (rather than traditional computer vision benchmarks) in boosting robustness to new domains.