  The rapid increase in the number of cyber-attacks in recent years raises the
need for principled methods for defending networks against malicious actors.
Deep reinforcement learning (DRL) has emerged as a promising approach for
mitigating these attacks. However, while DRL has shown much potential for
cyber-defence, numerous challenges must be overcome before DRL can be applied
to autonomous cyber-operations (ACO) at scale. Principled methods are required
for environments that confront learners with very high-dimensional state
spaces, large multi-discrete action spaces, and adversarial learning. Recent
works have reported success in solving these problems individually. There have
also been impressive engineering efforts towards solving all three for
real-time strategy games. However, applying DRL to the full ACO problem remains
an open challenge. Here, we survey the relevant DRL literature and
conceptualize an idealised ACO-DRL agent. We provide: i.) A summary of the
domain properties that define the ACO problem; ii.) A comprehensive evaluation
of the extent to which domains used for benchmarking DRL approaches are
comparable to ACO; iii.) An overview of state-of-the-art approaches for scaling
DRL to domains that confront learners with the curse of dimensionality, and;
iv.) A survey and critique of current methods for limiting the exploitability
of agents within adversarial settings from the perspective of ACO. We conclude
with open research questions that we hope will motivate future directions for
researchers and practitioners working on ACO.
