  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.
