Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and
  Gameplay constraints   Dynamic difficulty adjustment ($DDA$) is a process of automatically changing
a game difficulty for the optimization of user experience. It is a vital part
of almost any modern game. Most existing DDA approaches concentrate on the
experience of a player without looking at the rest of the players. We propose a
method that automatically optimizes user experience while taking into
consideration other players and macro constraints imposed by the game. The
method is based on deep neural network architecture that involves a count loss
constraint that has zero gradients in most of its support. We suggest a method
to optimize this loss function and provide theoretical analysis for its
performance. Finally, we provide empirical results of an internal experiment
that was done on $200,000$ players and was found to outperform the
corresponding manual heuristics crafted by game design experts.

20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph