  Given the success of reinforcement learning (RL) in various domains, it is
promising to explore the application of its methods to the development of
intelligent and autonomous cyber agents. Enabling this development requires a
representative RL training environment. To that end, this work presents CyGIL:
an experimental testbed of an emulated RL training environment for network
cyber operations. CyGIL uses a stateless environment architecture and
incorporates the MITRE ATT&CK framework to establish a high fidelity training
environment, while presenting a sufficiently abstracted interface to enable RL
training. Its comprehensive action space and flexible game design allow the
agent training to focus on particular advanced persistent threat (APT)
profiles, and to incorporate a broad range of potential threats and
vulnerabilities. By striking a balance between fidelity and simplicity, it aims
to leverage state of the art RL algorithms for application to real-world cyber
defence.
