Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Deep Reinforcement Learning Approach for Audio-based Navigation and
  Audio Source Localization in Multi-speaker Environments   In this work we apply deep reinforcement learning to the problems of
navigating a three-dimensional environment and inferring the locations of human
speaker audio sources within, in the case where the only available information
is the raw sound from the environment, as a simulated human listener placed in
the environment would hear it. For this purpose we create two virtual
environments using the Unity game engine, one presenting an audio-based
navigation problem and one presenting an audio source localization problem. We
also create an autonomous agent based on PPO online reinforcement learning
algorithm and attempt to train it to solve these environments. Our experiments
show that our agent achieves adequate performance and generalization ability in
both environments, measured by quantitative metrics, even when a limited amount
of training data are available or the environment parameters shift in ways not
encountered during training. We also show that a degree of agent knowledge
transfer is possible between the environments.

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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: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-lat
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The categories of this paper are: physics
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The categories of this paper are: cmp-lg