  Autonomous Cyber Operations (ACO) involves the consideration of blue team
(defender) and red team (attacker) decision-making models in adversarial
scenarios. To support the application of machine learning algorithms to solve
this problem, and to encourage such practitioners to attend to problems in the
ACO setting, a suitable gym (toolkit for experiments) is necessary. We
introduce CybORG, a work-in-progress gym for ACO research. Driven by the need
to efficiently support reinforcement learning to train adversarial
decision-making models through simulation and emulation, our design differs
from prior related work. Our early evaluation provides some evidence that
CybORG is appropriate for our purpose and may provide a basis for advancing ACO
research towards practical applications.
