Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Annotating Motion Primitives for Simplifying Action Search in
  Reinforcement Learning   Reinforcement learning in large-scale environments is challenging due to the
many possible actions that can be taken in specific situations. We have
previously developed a means of constraining, and hence speeding up, the search
process through the use of motion primitives; motion primitives are sequences
of pre-specified actions taken across a state series. As a byproduct of this
work, we have found that if the motion primitives' motions and actions are
labeled, then the search can be sped up further. Since motion primitives may
initially lack such details, we propose a theoretically viewpoint-insensitive
and speed-insensitive means of automatically annotating the underlying motions
and actions. We do this through a differential-geometric, spatio-temporal
kinematics descriptor, which analyzes how the poses of entities in two motion
sequences change over time. We use this descriptor in conjunction with a
weighted-nearest-neighbor classifier to label the primitives using a limited
set of training examples.
  In our experiments, we achieve high motion and action annotation rates for
human-action-derived primitives with as few as one training sample. We also
demonstrate that reinforcement learning using accurately labeled trajectories
leads to high-performing policies more quickly than standard reinforcement
learning techniques. This is partly because motion primitives encode prior
domain knowledge and preempt the need to re-discover that knowledge during
training. It is also because agents can leverage the labels to systematically
ignore action classes that do not facilitate task objectives, thereby reducing
the action space.

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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph