Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Systematic Generalisation through Task Temporal Logic and Deep
  Reinforcement Learning   This work introduces a neuro-symbolic agent that combines deep reinforcement
learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e.,
never-seen-before, generalisation of formally specified instructions. In
particular, we present a neuro-symbolic framework where a symbolic module
transforms TL specifications into a form that helps the training of a DRL agent
targeting generalisation, while a neural module learns systematically to solve
the given tasks. We study the emergence of systematic learning in different
settings and find that the architecture of the convolutional layers is key when
generalising to new instructions. We also provide evidence that systematic
learning can emerge with abstract operators such as negation when learning from
a few training examples, which previous research have struggled with.

6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
2
The categories of this paper are: math
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol