  We present an embedding of stochastic optimal control problems, of the so
called path integral form, into reproducing kernel Hilbert spaces. Using
consistent, sample based estimates of the embedding leads to a model free,
non-parametric approach for calculation of an approximate solution to the
control problem. This formulation admits a decomposition of the problem into an
invariant and task dependent component. Consequently, we make much more
efficient use of the sample data compared to previous sample based approaches
in this domain, e.g., by allowing sample re-use across tasks. Numerical
examples on test problems, which illustrate the sample efficiency, are
provided.
