MARLA documentation
MARLA (Multi-Agent Reinforcement Learning Architecture) is a research platform for augmenting a reinforcement-learning offensive-security agent with an advisory, language-model-based Plan Maker.
Concretely: a recurrent-PPO agent plays NASimEmu scenarios directly. In the assisted variant, it can also learn when to pause and ask a frozen, schema-validated Plan Maker for advice on the current set of legal actions, then learns how much to trust that advice via a residual, per-step confidence-weighted adjustment to its own policy.
There is no “coordinator” agent and no shared blackboard: the RL Orchestrator drives the whole experiment lifecycle itself, and every piece of advisory traffic is validated by a Gatekeeper agent before it can affect a decision.
Note
This documentation describes the implementation as it exists in this repository.
Where to start
New to MARLA? Start with Installation and Quickstart.
Writing or editing an experiment YAML? See Configuration reference.
Want to understand how the agents talk to each other? See Architecture.
Looking for a specific CLI flag? See Command-line reference.
Wondering what
marla summarizeplots mean? See Metrics and plots.Digging into a specific module? See the API reference.