CyberBattleSim

The simulation is intentionally simplistic to prevent nefarious use and focus on specific security aspects, as stated in the README's philosophy, enabling safe experimentation without real-world risks.

It provides a Python-based Open AI Gym interface, allowing for straightforward training of reinforcement learning agents on cybersecurity scenarios, as highlighted in the key features.

Includes a basic stochastic defender agent that detects and mitigates attacks, facilitating the study of interaction between automated agents, which is core to the project's goals.

Supports custom network topologies and vulnerability sets, enabling varied experimentation, as mentioned in the extensible environments feature and contribution ideas.