  When the underlying conditional density is known, conditional expectations
can be computed analytically or numerically. When, however, such knowledge is
not available and instead we are given a collection of training data, the goal
of this work is to propose simple and purely data-driven means for estimating
directly the desired conditional expectation. Because conditional expectations
appear in the description of a number of stochastic optimization problems with
the corresponding optimal solution satisfying a system of nonlinear equations,
we extend our data-driven method to cover such cases as well. We test our
methodology by applying it to Optimal Stopping and Optimal Action Policy in
Reinforcement Learning.
