Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Tracking the Race Between Deep Reinforcement Learning and Imitation
  Learning -- Extended Version   Learning-based approaches for solving large sequential decision making
problems have become popular in recent years. The resulting agents perform
differently and their characteristics depend on those of the underlying
learning approach. Here, we consider a benchmark planning problem from the
reinforcement learning domain, the Racetrack, to investigate the properties of
agents derived from different deep (reinforcement) learning approaches. We
compare the performance of deep supervised learning, in particular imitation
learning, to reinforcement learning for the Racetrack model. We find that
imitation learning yields agents that follow more risky paths. In contrast, the
decisions of deep reinforcement learning are more foresighted, i.e., avoid
states in which fatal decisions are more likely. Our evaluations show that for
this sequential decision making problem, deep reinforcement learning performs
best in many aspects even though for imitation learning optimal decisions are
considered.

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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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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: plasm-ph