Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Decision Making by Generating and Following Natural
  Language Instructions   We explore using latent natural language instructions as an expressive and
compositional representation of complex actions for hierarchical decision
making. Rather than directly selecting micro-actions, our agent first generates
a latent plan in natural language, which is then executed by a separate model.
We introduce a challenging real-time strategy game environment in which the
actions of a large number of units must be coordinated across long time scales.
We gather a dataset of 76 thousand pairs of instructions and executions from
human play, and train instructor and executor models. Experiments show that
models using natural language as a latent variable significantly outperform
models that directly imitate human actions. The compositional structure of
language proves crucial to its effectiveness for action representation. We also
release our code, models and data.

24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn