Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Atari games and Intel processors   The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms.

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The categories of this paper are: chem-ph
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ph