Generalization and Regularization in DQN
Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. However, despite the ever-increasing performance on popular benchmarks like the Arcade Learning Environment (ALE), policies learned by deep RL algorithms can struggle to generalize when evaluated in remarkably similar environments. These results are unexpected given the fact that, in supervised learning, deep neural networks often learn robust features that generalize across tasks. In this paper, we study the generalization capabilities of DQN in order to aid in understanding this mismatch between generalization in deep RL and supervised learning methods. We provide evidence suggesting that DQN overspecializes to the domain it is trained on. We then comprehensively evaluate the impact of traditional methods of regularization from supervised learning, and dropout, and of reusing learned representations to improve the generalization capabilities of DQN. We perform this study using different game modes of Atari 2600 games, a recently introduced modification for the ALE which supports slight variations of the Atari 2600 games used for benchmarking in the field. Despite regularization being largely underutilized in deep RL, we show that it can, in fact, help DQN learn more general features. These features can then be reused and fine-tuned on similar tasks, considerably improving the sample efficiency of DQN.
 This is an empirical study on the ability for DQNs trained with/without regularization to perform well on variants of the same environment (e.g. increasing difficulty of a game). The paper is well written, the experimental methodology is clear & sound, and the significance is around improved sample efficiency via warm starting from a regularized DQN to fine tune. The error bounds for the regularized models results seem uncomfortably large in some cases. Overall it looks like a good methodological paper that can inform others on taking regularization more seriously when training DQNs. Evaluating on a modified ALE environment is great, but it would have been better to see this having similar impact in real life applications.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Generalization and Regularization in DQN
Deep reinforcement learning (RL) algorithms have shown an impressive ability to learn complex control policies in high-dimensional environments. However, despite the ever-increasing performance on popular benchmarks like the Arcade Learning Environment (ALE), policies learned by deep RL algorithms can struggle to generalize when evaluated in remarkably similar environments. These results are unexpected given the fact that, in supervised learning, deep neural networks often learn robust features that generalize across tasks. In this paper, we study the generalization capabilities of DQN in order to aid in understanding this mismatch between generalization in deep RL and supervised learning methods. We provide evidence suggesting that DQN overspecializes to the domain it is trained on. We then comprehensively evaluate the impact of traditional methods of regularization from supervised learning, and dropout, and of reusing learned representations to improve the generalization capabilities of DQN. We perform this study using different game modes of Atari 2600 games, a recently introduced modification for the ALE which supports slight variations of the Atari 2600 games used for benchmarking in the field. Despite regularization being largely underutilized in deep RL, we show that it can, in fact, help DQN learn more general features. These features can then be reused and fine-tuned on similar tasks, considerably improving the sample efficiency of DQN.
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 is an empirical study on the ability for DQNs trained with/without regularization to perform well on variants of the same environment (e.g. increasing difficulty of a game). The paper is well written, the experimental methodology is clear & sound, and the significance is around improved sample efficiency via warm starting from a regularized DQN to fine tune. The error bounds for the regularized models results seem uncomfortably large in some cases. Overall it looks like a good methodological paper that can inform others on taking regularization more seriously when training DQNs. Evaluating on a modified ALE environment is great, but it would have been better to see this having similar impact in real life applications.
assistant
 This is an empirical study on the ability for DQNs trained with/without regularization to perform well on variants of the same environment (e.g. increasing difficulty of a game). The paper is well written, the experimental methodology is clear & sound, and the significance is around improved sample efficiency via warm starting from a regularized DQN to fine tune. The error bounds for the regularized models results seem uncomfortably large in some cases. Overall it looks like a good methodological paper that can inform others on taking regularization more seriously when training DQNs. Evaluating on a modified ALE environment is great, but it would have been better to see this having similar impact in real life applications.