  Autonomous cyber agents may be developed by applying reinforcement and deep
reinforcement learning (RL/DRL), where agents are trained in a representative
environment. The training environment must simulate with high-fidelity the
network Cyber Operations (CyOp) that the agent aims to explore. Given the
complexity of net-work CyOps, a good simulator is difficult to achieve. This
work presents a systematic solution to automatically generate a high-fidelity
simulator in the Cyber Gym for Intelligent Learning (CyGIL). Through
representation learning and continuous learning, CyGIL provides a unified CyOp
training environment where an emulated CyGIL-E automatically generates a
simulated CyGIL-S. The simulator generation is integrated with the agent
training process to further reduce the required agent training time. The agent
trained in CyGIL-S is transferrable directly to CyGIL-E showing full
transferability to the emulated "real" network. Experimental results are
presented to demonstrate the CyGIL training performance. Enabling offline RL,
the CyGIL solution presents a promising direction towards sim-to-real for
leveraging RL agents in real-world cyber networks.

Machine Learning;Artificial Intelligence;Cryptography and Security