Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Simulation Optimization to Improve Zero-shot Policy Transfer of
  Quadrotors   In this work, we show that it is possible to train low-level control policies
with reinforcement learning entirely in simulation and, then, deploy them on a
quadrotor robot without using real-world data to fine-tune. To render zero-shot
policy transfers feasible, we apply simulation optimization to narrow the
reality gap. Our neural network-based policies use only onboard sensor data and
run entirely on the embedded drone hardware. In extensive real-world
experiments, we compare three different control structures ranging from
low-level pulse-width-modulated motor commands to high-level attitude control
based on nested proportional-integral-derivative controllers. Our experiments
show that low-level controllers trained with reinforcement learning require a
more accurate simulation than higher-level control policies.

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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci