  Agent-based models (ABMs) are a promising approach to modelling and reasoning
about complex systems, yet their application in practice is impeded by their
complexity, discrete nature, and the difficulty of performing parameter
inference and optimisation tasks. This in turn has sparked interest in the
construction of differentiable ABMs as a strategy for combatting these
difficulties, yet a number of challenges remain. In this paper, we discuss and
present experiments that highlight some of these challenges, along with
potential solutions.
