Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inducing Functions through Reinforcement Learning without Task
  Specification   We report a bio-inspired framework for training a neural network through
reinforcement learning to induce high level functions within the network. Based
on the interpretation that animals have gained their cognitive functions such
as object recognition - without ever being specifically trained for - as a
result of maximizing their fitness to the environment, we place our agent in an
environment where developing certain functions may facilitate decision making.
The experimental results show that high level functions, such as image
classification and hidden variable estimation, can be naturally and
simultaneously induced without any pre-training or specifying them.

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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: econ