  We present a reinforcement learning algorithm for learning sparse
non-parametric controllers in a Reproducing Kernel Hilbert Space. We improve
the sample complexity of this approach by imposing a structure of the
state-action function through a normalized advantage function (NAF). This
representation of the policy enables efficiently composing multiple learned
models without additional training samples or interaction with the environment.
We demonstrate the performance of this algorithm on learning obstacle-avoidance
policies in multiple simulations of a robot equipped with a laser scanner while
navigating in a 2D environment. We apply the composition operation to various
policy combinations and test them to show that the composed policies retain the
performance of their components. We also transfer the composed policy directly
to a physical platform operating in an arena with obstacles in order to
demonstrate a degree of generalization.
