  We present a novel online learning algorithm for a class of unknown and
uncertain dynamical environments that are fully observable. First, we obtain a
novel probabilistic characterization of systems whose mean behavior is known
but which are subject to additive, unknown subGaussian disturbances. This
characterization relies on recent concentration of measure results and is given
in terms of ambiguity sets. Second, we extend the results to environments whose
mean behavior is also unknown but described by a parameterized class of
possible mean behaviors. Our algorithm adapts the ambiguity set dynamically by
learning the parametric dependence online, and retaining similar probabilistic
guarantees with respect to the additive, unknown disturbance. We illustrate the
results on a differential-drive robot subject to environmental uncertainty.
