  Volumetric modulated arc therapy planning is a challenging problem in
high-dimensional, non-convex optimization. Traditionally, heuristics such as
fluence-map-optimization-informed segment initialization use locally optimal
solutions to begin the search of the full arc therapy plan space from a
reasonable starting point. These routines facilitate arc therapy optimization
such that clinically satisfactory radiation treatment plans can be created in
about 10 minutes. However, current optimization algorithms favor solutions near
their initialization point and are slower than necessary due to plan
overparameterization. In this work, arc therapy overparameterization is
addressed by reducing the effective dimension of treatment plans with
unsupervised deep learning. An optimization engine is then built based on
low-dimensional arc representations which facilitates faster planning times.
