Annotating Motion Primitives for Simplifying Action Search in
Reinforcement Learning
- URL: http://arxiv.org/abs/2102.12017v1
- Date: Wed, 24 Feb 2021 01:32:06 GMT
- Title: Annotating Motion Primitives for Simplifying Action Search in
Reinforcement Learning
- Authors: Isaac J. Sledge and Darshan W. Bryner and Jose C. Principe
- Abstract summary: 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.
We propose a theoretically viewpoint-insensitive and speed-insensitive means of automatically annotating the underlying motions and actions.
- Score: 10.764160559530847
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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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