Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning with a Combinatorial Action Space for
  Predicting Popular Reddit Threads   We introduce an online popularity prediction and tracking task as a benchmark
task for reinforcement learning with a combinatorial, natural language action
space. A specified number of discussion threads predicted to be popular are
recommended, chosen from a fixed window of recent comments to track. Novel deep
reinforcement learning architectures are studied for effective modeling of the
value function associated with actions comprised of interdependent sub-actions.
The proposed model, which represents dependence between sub-actions through a
bi-directional LSTM, gives the best performance across different experimental
configurations and domains, and it also generalizes well with varying numbers
of recommendation requests.

3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th