Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic
  Manipulation via Discretisation   We present a coarse-to-fine discretisation method that enables the use of
discrete reinforcement learning approaches in place of unstable and
data-inefficient actor-critic methods in continuous robotics domains. This
approach builds on the recently released ARM algorithm, which replaces the
continuous next-best pose agent with a discrete one, with coarse-to-fine
Q-attention. Given a voxelised scene, coarse-to-fine Q-attention learns what
part of the scene to 'zoom' into. When this 'zooming' behaviour is applied
iteratively, it results in a near-lossless discretisation of the translation
space, and allows the use of a discrete action, deep Q-learning method. We show
that our new coarse-to-fine algorithm achieves state-of-the-art performance on
several difficult sparsely rewarded RLBench vision-based robotics tasks, and
can train real-world policies, tabula rasa, in a matter of minutes, with as
little as 3 demonstrations.

35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas