This work aims to enable autonomous agents for network cyber operations
(CyOps) by applying reinforcement and deep reinforcement learning (RL/DRL). The
required RL training environment is particularly challenging, as it must
balance the need for high-fidelity, best achieved through real network
emulation, with the need for running large numbers of training episodes, best
achieved using simulation. A unified training environment, namely the Cyber Gym
for Intelligent Learning (CyGIL) is developed where an emulated CyGIL-E
automatically generates a simulated CyGIL-S. From preliminary experimental
results, CyGIL-S is capable to train agents in minutes compared with the days
required in CyGIL-E. The agents trained in CyGIL-S are transferrable directly
to CyGIL-E showing full decision proficiency in the emulated "real" network.
Enabling offline RL, the CyGIL solution presents a promising direction towards
sim-to-real for leveraging RL agents in real-world cyber networks.

本文旨在通过应用增强学习和深度增强学习（RL/DRL）来使网络网络运营（CyOps）中的自主智能体成为可能，并且通过提出的一个名为 Cyber Gym for Intelligent Learning（CyGIL）的统一训练环境，尤其是最近发展出的一种在仿真环境下训练智能体的解决方案，该方案旨在在实际网络环境中实现从仿真到现实的自主智能体应用。