Learning Representations in Model-Free Hierarchical Reinforcement Learning
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction. Abstraction can be had by identifying a relatively small set of states that are likely to be useful as subgoals, in concert with the learning of corresponding skill policies to achieve those subgoals. Many approaches to subgoal discovery in HRL depend on the analysis of a model of the environment, but the need to learn such a model introduces its own problems of scale. Once subgoals are identified, skills may be learned through intrinsic motivation, introducing an internal reward signal marking subgoal attainment. In this paper, we present a novel model-free method for subgoal discovery using incremental unsupervised learning over a small memory of the most recent experiences of the agent. When combined with an intrinsic motivation learning mechanism, this method learns subgoals and skills together, based on experiences in the environment. Thus, we offer an original approach to HRL that does not require the acquisition of a model of the environment, suitable for large-scale applications. We demonstrate the efficiency of our method on two RL problems with sparse delayed feedback: a variant of the rooms environment and the ATARI 2600 game called Montezuma*s Revenge.
 This paper proposed a model-free HRL method, which is combined with unsupervised learning methods, including abnormality discovery and clustering for subgoal discovery. In all, this paper studies a very important problem in RL and is easy to follow. The technique is sound. Although the novelty is not that significant (combining existing techniques), it showed good results on Montezuma’ revenge, which is considered as a very challenging problem for primitive action based RL. Although the results are impressive, I still have some doubt about the generalizability of the method. It might be helpful to improve its significance if more diversified domains can be tested. The paper can be strengthen by providing some ablation test, for example, is performance under different K for Kmeans? Also some important details seems missing, for example, the data used for kmeans, it is mentioned that the input to the controller is four consecutive frame of size 84x84, so the input data dimension is more than 10k, I guess some further dimensionality reduction technique has to be applied in order to run kmeans effectively. Regarding the comparisons, the proposed method is only compared with one primitive action based method. It might be better to include results from other HRL methods, such as Kulkarni et al. Is the curve based on the mean of different runs? It might be useful to include an errorbar to show the statistical significance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning Representations in Model-Free Hierarchical Reinforcement Learning
Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of temporal abstraction. Abstraction can be had by identifying a relatively small set of states that are likely to be useful as subgoals, in concert with the learning of corresponding skill policies to achieve those subgoals. Many approaches to subgoal discovery in HRL depend on the analysis of a model of the environment, but the need to learn such a model introduces its own problems of scale. Once subgoals are identified, skills may be learned through intrinsic motivation, introducing an internal reward signal marking subgoal attainment. In this paper, we present a novel model-free method for subgoal discovery using incremental unsupervised learning over a small memory of the most recent experiences of the agent. When combined with an intrinsic motivation learning mechanism, this method learns subgoals and skills together, based on experiences in the environment. Thus, we offer an original approach to HRL that does not require the acquisition of a model of the environment, suitable for large-scale applications. We demonstrate the efficiency of our method on two RL problems with sparse delayed feedback: a variant of the rooms environment and the ATARI 2600 game called Montezuma*s Revenge.
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 paper proposed a model-free HRL method, which is combined with unsupervised learning methods, including abnormality discovery and clustering for subgoal discovery. In all, this paper studies a very important problem in RL and is easy to follow. The technique is sound. Although the novelty is not that significant (combining existing techniques), it showed good results on Montezuma’ revenge, which is considered as a very challenging problem for primitive action based RL. Although the results are impressive, I still have some doubt about the generalizability of the method. It might be helpful to improve its significance if more diversified domains can be tested. The paper can be strengthen by providing some ablation test, for example, is performance under different K for Kmeans? Also some important details seems missing, for example, the data used for kmeans, it is mentioned that the input to the controller is four consecutive frame of size 84x84, so the input data dimension is more than 10k, I guess some further dimensionality reduction technique has to be applied in order to run kmeans effectively. Regarding the comparisons, the proposed method is only compared with one primitive action based method. It might be better to include results from other HRL methods, such as Kulkarni et al. Is the curve based on the mean of different runs? It might be useful to include an errorbar to show the statistical significance.
assistant
 This paper proposed a model-free HRL method, which is combined with unsupervised learning methods, including abnormality discovery and clustering for subgoal discovery. In all, this paper studies a very important problem in RL and is easy to follow. The technique is sound. Although the novelty is not that significant (combining existing techniques), it showed good results on Montezuma’ revenge, which is considered as a very challenging problem for primitive action based RL. Although the results are impressive, I still have some doubt about the generalizability of the method. It might be helpful to improve its significance if more diversified domains can be tested. The paper can be strengthen by providing some ablation test, for example, is performance under different K for Kmeans? Also some important details seems missing, for example, the data used for kmeans, it is mentioned that the input to the controller is four consecutive frame of size 84x84, so the input data dimension is more than 10k, I guess some further dimensionality reduction technique has to be applied in order to run kmeans effectively. Regarding the comparisons, the proposed method is only compared with one primitive action based method. It might be better to include results from other HRL methods, such as Kulkarni et al. Is the curve based on the mean of different runs? It might be useful to include an errorbar to show the statistical significance.