  Neuro-dynamic programming is a class of powerful techniques for approximating
the solution to dynamic programming equations. In their most computationally
attractive formulations, these techniques provide the approximate solution only
within a prescribed finite-dimensional function class. Thus, the question that
always arises is how should the function class be chosen? The goal of this
paper is to propose an approach using the solutions to associated fluid and
diffusion approximations. In order to illustrate this approach, the paper
focuses on an application to dynamic speed scaling for power management in
computer processors.
