Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Modular Control for Embodied Question Answering   We present a modular approach for learning policies for navigation over long
planning horizons from language input. Our hierarchical policy operates at
multiple timescales, where the higher-level master policy proposes subgoals to
be executed by specialized sub-policies. Our choice of subgoals is
compositional and semantic, i.e. they can be sequentially combined in arbitrary
orderings, and assume human-interpretable descriptions (e.g. 'exit room', 'find
kitchen', 'find refrigerator', etc.).
  We use imitation learning to warm-start policies at each level of the
hierarchy, dramatically increasing sample efficiency, followed by reinforcement
learning. Independent reinforcement learning at each level of hierarchy enables
sub-policies to adapt to consequences of their actions and recover from errors.
Subsequent joint hierarchical training enables the master policy to adapt to
the sub-policies.
  On the challenging EQA (Das et al., 2018) benchmark in House3D (Wu et al.,
2018), requiring navigating diverse realistic indoor environments, our approach
outperforms prior work by a significant margin, both in terms of navigation and
question answering.

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The categories of this paper are: atom-ph
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci