Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient-Descent for Randomized Controllers under Partial Observability   Randomization is a powerful technique to create robust controllers, in
particular in partially observable settings. The degrees of randomization have
a significant impact on the system performance, yet they are intricate to get
right. The use of synthesis algorithms for parametric Markov chains (pMCs) is a
promising direction to support the design process of such controllers. This
paper shows how to define and evaluate gradients of pMCs. Furthermore, it
investigates varieties of gradient descent techniques from the machine learning
community to synthesize the probabilities in a pMC. The resulting method scales
to significantly larger pMCs than before and empirically outperforms the
state-of-the-art, often by at least one order of magnitude.

3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org