I will provide a brief overview of a class stochastic optimal control problems recently developed by our group as well as by Bert Kappen's group. This problem class is quite general and yet has a number of unique properties, including linearity of the exponentially-transformed (Hamilton-Jacobi) Bellman equation, duality with Bayesian inference, convexity of the inverse optimal control problem, compositionality of optimal control laws, path-integral representation of the exponentially-transformed value function. I will then focus on function approximation methods that exploit the linearity of the Bellman equation, and illustrate how such methods scale to high-dimensional continuous dynamical systems. Computing the weights for a fixed set of basis functions can be done very efficiently by solving a large but sparse linear problem. This enables us to work with hundreds of millions of (localized) bases. Still, the volume of a high-dimensional state space is too large to be filled with localized bases, forcing us to consider adaptive methods for positioning and shaping those bases. Several such methods will be compared.