How many weights are enough : can tensor factorization learn efficient policies ?
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we employ tensor factorization in order to learn more compact representations for reinforcement learning policies. We show empirically that in the low-data regime, it is possible to learn online policies with 2 to 10 times less total coefficients, with little to no loss of performance. We also leverage progress in second order optimization, and use the theory of wavelet scattering to further reduce the number of learned coefficients, by foregoing learning the topmost convolutional layer filters altogether. We evaluate our results on the Atari suite against recent baseline algorithms that represent the state-of-the-art in data efficiency, and get comparable results with an order of magnitude gain in weight parsimony.
This paper suggests three different disconnected ideas for improving the number of parameters of deep vision models for playing ATARI and to improve the training speed. - Tensor-regression layer to replace fully connected layers - Wavelet-scattering layer to replace the first convolutional layer - Second order optimization (K-FAC) All the ideas mentioned in this paper are existing ones (although properly attributed), so the novelty of this work is relatively low. The paper mentions that this particular combination is *novel*, but it is not clear is there is any significant synergy between these methods and why it should be considered interesting in this particular setup. Also the paper conflates sample-efficiency with parameter-efficiency. However, there is no indication that any of these methods address sample-efficiency which would be an interesting and useful contribution. Also the experiments are neither very conclusive nor are they easy to interpret. For example in the pong case, there is no discernable effect of the compression ratio as the highest and lowest compression give the best (and comparable) results. Also the results come without confidence intervals. So, in general, I would consider this paper to be an uninspired combination of pre-existing ideas with weak and inconclusive experimental results: a clear reject.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
How many weights are enough : can tensor factorization learn efficient policies ?
Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation. In this work, we employ tensor factorization in order to learn more compact representations for reinforcement learning policies. We show empirically that in the low-data regime, it is possible to learn online policies with 2 to 10 times less total coefficients, with little to no loss of performance. We also leverage progress in second order optimization, and use the theory of wavelet scattering to further reduce the number of learned coefficients, by foregoing learning the topmost convolutional layer filters altogether. We evaluate our results on the Atari suite against recent baseline algorithms that represent the state-of-the-art in data efficiency, and get comparable results with an order of magnitude gain in weight parsimony.
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 suggests three different disconnected ideas for improving the number of parameters of deep vision models for playing ATARI and to improve the training speed. - Tensor-regression layer to replace fully connected layers - Wavelet-scattering layer to replace the first convolutional layer - Second order optimization (K-FAC) All the ideas mentioned in this paper are existing ones (although properly attributed), so the novelty of this work is relatively low. The paper mentions that this particular combination is *novel*, but it is not clear is there is any significant synergy between these methods and why it should be considered interesting in this particular setup. Also the paper conflates sample-efficiency with parameter-efficiency. However, there is no indication that any of these methods address sample-efficiency which would be an interesting and useful contribution. Also the experiments are neither very conclusive nor are they easy to interpret. For example in the pong case, there is no discernable effect of the compression ratio as the highest and lowest compression give the best (and comparable) results. Also the results come without confidence intervals. So, in general, I would consider this paper to be an uninspired combination of pre-existing ideas with weak and inconclusive experimental results: a clear reject.
assistant
This paper suggests three different disconnected ideas for improving the number of parameters of deep vision models for playing ATARI and to improve the training speed. - Tensor-regression layer to replace fully connected layers - Wavelet-scattering layer to replace the first convolutional layer - Second order optimization (K-FAC) All the ideas mentioned in this paper are existing ones (although properly attributed), so the novelty of this work is relatively low. The paper mentions that this particular combination is *novel*, but it is not clear is there is any significant synergy between these methods and why it should be considered interesting in this particular setup. Also the paper conflates sample-efficiency with parameter-efficiency. However, there is no indication that any of these methods address sample-efficiency which would be an interesting and useful contribution. Also the experiments are neither very conclusive nor are they easy to interpret. For example in the pong case, there is no discernable effect of the compression ratio as the highest and lowest compression give the best (and comparable) results. Also the results come without confidence intervals. So, in general, I would consider this paper to be an uninspired combination of pre-existing ideas with weak and inconclusive experimental results: a clear reject.