Large-Scale Study of Curiosity-Driven Learning
Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is such intrinsic reward function which uses prediction error as a reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. {em without any extrinsic rewards}, across standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance as well as a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many games. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://doubleblindsupplementary.github.io/large-curiosity/.
 In this paper, the authors presented a large experimental study of curiosity-driven reinforcement learning on various tasks. In the experimental studies, the authors also compared several feature space embedding methods, including identical mapping (pixels), random embedding, variational autoencoders and inverse dynamics features. The authors found that in many of the tasks, learning based on intrinsic rewards could generate good performance on extrinsic rewards, when the intrinsic rewards and extrinsic rewards are correlated. The authors also found that random features embedding, somewhat surprisingly, performs well in the tasks. Overall, the paper is well written with clarity. Experimental setup is easy to understand. The authors provided code, which could help other researchers reproduce their result. Weaknesses: 1) as an experimental study, it would be valuable to compare the performance of curiosity-based learning versus learning based on well-defined extrinsic rewards. The author is correct that in many tasks, well-behaved extrinsic rewards are hard to find. But for problems with well-defined extrinsic rewards, such a comparison could help readers understand the relative performance of curiosity-based learning and/or how much headroom there exists to improve the current methods. 2) it is surprising that random features perform so well in the experiments. The authors did provide literature in classification that had similar findings, but it would be beneficial for the authors to explore reasons that random features perform well in reinforcement learning.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Large-Scale Study of Curiosity-Driven Learning
Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is such intrinsic reward function which uses prediction error as a reward signal. In this paper: (a) We perform the first large-scale study of purely curiosity-driven learning, i.e. {em without any extrinsic rewards}, across standard benchmark environments, including the Atari game suite. Our results show surprisingly good performance as well as a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many games. (b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). (c) We demonstrate limitations of the prediction-based rewards in stochastic setups. Game-play videos and code are at https://doubleblindsupplementary.github.io/large-curiosity/.
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>



 In this paper, the authors presented a large experimental study of curiosity-driven reinforcement learning on various tasks. In the experimental studies, the authors also compared several feature space embedding methods, including identical mapping (pixels), random embedding, variational autoencoders and inverse dynamics features. The authors found that in many of the tasks, learning based on intrinsic rewards could generate good performance on extrinsic rewards, when the intrinsic rewards and extrinsic rewards are correlated. The authors also found that random features embedding, somewhat surprisingly, performs well in the tasks. Overall, the paper is well written with clarity. Experimental setup is easy to understand. The authors provided code, which could help other researchers reproduce their result. Weaknesses: 1) as an experimental study, it would be valuable to compare the performance of curiosity-based learning versus learning based on well-defined extrinsic rewards. The author is correct that in many tasks, well-behaved extrinsic rewards are hard to find. But for problems with well-defined extrinsic rewards, such a comparison could help readers understand the relative performance of curiosity-based learning and/or how much headroom there exists to improve the current methods. 2) it is surprising that random features perform so well in the experiments. The authors did provide literature in classification that had similar findings, but it would be beneficial for the authors to explore reasons that random features perform well in reinforcement learning.
assistant
 In this paper, the authors presented a large experimental study of curiosity-driven reinforcement learning on various tasks. In the experimental studies, the authors also compared several feature space embedding methods, including identical mapping (pixels), random embedding, variational autoencoders and inverse dynamics features. The authors found that in many of the tasks, learning based on intrinsic rewards could generate good performance on extrinsic rewards, when the intrinsic rewards and extrinsic rewards are correlated. The authors also found that random features embedding, somewhat surprisingly, performs well in the tasks. Overall, the paper is well written with clarity. Experimental setup is easy to understand. The authors provided code, which could help other researchers reproduce their result. Weaknesses: 1) as an experimental study, it would be valuable to compare the performance of curiosity-based learning versus learning based on well-defined extrinsic rewards. The author is correct that in many tasks, well-behaved extrinsic rewards are hard to find. But for problems with well-defined extrinsic rewards, such a comparison could help readers understand the relative performance of curiosity-based learning and/or how much headroom there exists to improve the current methods. 2) it is surprising that random features perform so well in the experiments. The authors did provide literature in classification that had similar findings, but it would be beneficial for the authors to explore reasons that random features perform well in reinforcement learning.