Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probabilistic design of optimal sequential decision-making algorithms in
  learning and control   This survey is focused on certain sequential decision-making problems that
involve optimizing over probability functions. We discuss the relevance of
these problems for learning and control. The survey is organized around a
framework that combines a problem formulation and a set of resolution methods.
The formulation consists of an infinite-dimensional optimization problem. The
methods come from approaches to search optimal solutions in the space of
probability functions. Through the lenses of this overarching framework we
revisit popular learning and control algorithms, showing that these naturally
arise from suitable variations on the formulation mixed with different
resolution methods. A running example, for which we make the code available,
complements the survey. Finally, a number of challenges arising from the survey
are also outlined.

15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol