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name: agents description: "Implement and debug NAVSIM rule-based, privileged, learned, TransFuser, and latent TransFuser agents while preserving sensor, trajectory, feature-builder, and checkpoint contracts." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

NAVSIM agents

Use this route when an agent must be selected, implemented, trained, loaded, or troubleshot. It covers the AbstractAgent interface, rule-based and privileged baselines, ego-status and TransFuser/LTF learned agents, sensor history, local trajectory sampling, feature/target builders, and checkpoint loading.

Route by intent

• Interface or API design: read API reference.
• Sensor selection or output shape: read trajectory and sensors.
• Baseline choice or learned-agent extension: read baselines.
• An error or failed run: read troubleshooting and run the safe validator before touching data or launching a workload.

Required operating rules

1. Subclass AbstractAgent, expose a stable name(), initialize once per worker, return a deliberate SensorConfig, and return local rear-axle BEV (x, y, heading) poses in a Trajectory.
2. Treat the trajectory sampling object as part of the model contract. The repository default requests a 4-second horizon sampled every 0.5 seconds; evaluation uses a 4-second, 0.1-second proposal. Do not silently change one to satisfy the other.
3. A submission receives AgentInput, not an annotated Scene. Keep requires_scene=False for submission agents; the human/privileged baseline is useful for analysis but is prohibited in submission creation.
4. Choose sensors explicitly. TransFuser uses the current front camera trio and current LiDAR by default; latent TransFuser replaces the LiDAR branch with a learned latent tensor and must not request or build a LiDAR feature.
5. Learned agents must keep feature-builder keys, target-builder outputs, forward() output (including "trajectory" with shape [B, T, 3]), loss, optimizer, and checkpoint key namespaces mutually compatible.

Safe verification

Run the bundled agent contract validator:

python scripts/validate_agent_contract.py
python scripts/validate_agent_contract.py --help

The validator uses synthetic arrays and sensor configurations only. It does not require a dataset, maps, checkpoints, downloads, submission credentials, or a benchmark. Add --module path/to/your_agent.py to inspect importable agent classes without instantiating or running them; use --strict when missing optional NAVSIM dependencies should fail the check.

Keep training, scoring, submission generation, and dataset access on their respective workflow routes. This sub-skill explains how an agent participates in those workflows but does not launch them by default.