  Central to all machine learning algorithms is data representation. For
multi-agent systems, selecting a representation which adequately captures the
interactions among agents is challenging due to the latent group structure
which tends to vary depending on context. However, in multi-agent systems with
strong group structure, we can simultaneously learn this structure and map a
set of agents to a consistently ordered representation for further learning. In
this paper, we present a dynamic alignment method which provides a robust
ordering of structured multi-agent data enabling representation learning to
occur in a fraction of the time of previous methods. We demonstrate the value
of this approach using a large amount of soccer tracking data from a
professional league.
