Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep neural networks algorithms for stochastic control problems on
  finite horizon: numerical applications   This paper presents several numerical applications of deep learning-based
algorithms that have been introduced in [HPBL18]. Numerical and comparative
tests using TensorFlow illustrate the performance of our different algorithms,
namely control learning by performance iteration (algorithms NNcontPI and
ClassifPI), control learning by hybrid iteration (algorithms Hybrid-Now and
Hybrid-LaterQ), on the 100-dimensional nonlinear PDEs examples from [EHJ17] and
on quadratic backward stochastic differential equations as in [CR16]. We also
performed tests on low-dimension control problems such as an option hedging
problem in finance, as well as energy storage problems arising in the valuation
of gas storage and in microgrid management. Numerical results and comparisons
to quantization-type algorithms Qknn, as an efficient algorithm to numerically
solve low-dimensional control problems, are also provided; and some
corresponding codes are available on https://github.com/comeh/.

8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an