Exploring the spectrum of novel behaviors a physical system can produce can
be a labor-intensive task. Active learning is a collection of iterative
sampling techniques developed in response to this challenge. However, these
techniques often require a pre-defined metric, such as distance in a space of
known order parameters, in order to guide the search for new behaviors. Order
parameters are rarely known for non-equilibrium systems \textit{a priori},
especially when possible behaviors are also unknown, creating a chicken-and-egg
problem. Here, we combine active and unsupervised learning for automated
exploration of novel behaviors in non-equilibrium systems with unknown order
parameters. We iteratively use active learning based on current order
parameters to expand the library of known behaviors and then relearn order
parameters based on this expanded library. We demonstrate the utility of this
approach in Kuramoto models of coupled oscillators of increasing complexity. In
addition to reproducing known phases, we also reveal previously unknown
behavior and related order parameters