PrimAITE (Primary-level AI Training Environment) is a simulation environment for training AI under the ARCD programme. It incorporates the functionality required of a Primary-level environment, as specified in the Dstl ARCD Training Environment Matrix document:
* The ability to model a relevant platform / system context;
* The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems, file system, services and processes;
* Operates at machine-speed to enable fast training cycles.
PrimAITE aims to evolve into an ARCD environment that could be used as the follow-on from Reception level approaches (e.g. `Yawning-Titan <https://github.com/dstl/YAWNING-TITAN>`_), and help bridge the Sim-to-Real gap into Secondary level environments.
*`OpenAI's Gym <https://gym.openai.com/>`_ is used as the basis for AI blue agent interaction with the PrimAITE environment
*`Networkx <https://github.com/networkx/networkx>`_ is used as the underlying data structure used for the PrimAITE environment
*`Stable Baselines 3 <https://github.com/DLR-RM/stable-baselines3>`_ is used as a default source of RL algorithms (although PrimAITE is not limited to SB3 agents)
*`Jupyterlab <https://github.com/jupyterlab/jupyterlab>`_ is used as an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture
*`Platformdirs <https://github.com/platformdirs/platformdirs>`_ is used for finding the right location to store user data and configuration but varies per platform
*`Plotly <https://github.com/plotly/plotly.py>`_ is used for building high level charts
