The ARCD Primary-level AI Training Environment (**PrimAITE**) provides an effective simulation capability for the purposes of training and evaluating AI in a cyber-defensive role. It incorporates the functionality required of a primary-level ARCD environment, which includes:
- The ability to model key characteristics of a platform / system by representing connections, IP addresses, ports, traffic loading, operating systems and services;
- A Reinforcement Learning (RL) reward function based on (a) the ability to counter the specific modelled adversarial cyber-attack, and (b) the ability to ensure success;
- Support for multiple agents, each having their own customisable observation space, action space, and reward function definition, and either deterministic or RL-directed behaviour
Whilst PrimAITE ships with a number of example modelled scenarios (a.k.a. Use Cases), it has not been developed to mandate the solving of a single cyber challenge, and instead provides a highly flexible environment application that can be extended and reconfigured by the user to suit their specific cyber defence training and evaluation needs. PrimAITE provides default networks, red agent and green agent behaviour, reward functions, and action / observation space configuration, all of which can be utilised out of the box, but which ultimately can (and in some instances should) be built upon and / or reconfigured to meet the needs of different defensive agent developers. The PrimAITE user guide provides comprehensive instruction on all PrimAITE features, functionality and components, and can be consulted in order to help guide users in any reconfiguration or enhancements they wish to undertake; a library of example Jupyter notebooks are also provided to support such work.
*It is possible to install PrimAITE without Ray RLLib, StableBaselines3, or any deep learning libraries by omitting the `rl` flag in the pip install command.*
Use the provided jupyter notebooks as a starting point to try running PrimAITE. They are automatically copied to your PrimAITE notebook folder when you run `primaite setup`.
#### 1. Activate the virtual environment
##### Windows (Powershell)
```powershell
.\venv\Scripts\activate
```
##### Unix
```bash
source venv/bin/activate
```
#### 2. Open jupyter notebook
```bash
python -m jupyter notebook
```
Then, click the URL provided by the jupyter command to open the jupyter application in your browser. You can also open notebooks in your IDE if supported.
Check out the example notebooks to learn more about how PrimAITE works and how you can use it to train agents. They are automatically copied to your primaite installation directory when you run `primaite setup`.
