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name: evaluation-and-artifacts description: "Evaluate and inspect local RL Baselines3 Zoo model artifacts without rendering or live Hub side effects." disable-model-invocation: true metadata: disco-role: operating license: MIT

Evaluation and Artifacts

Use this sub-skill when a future Researcher needs to load, evaluate, or inspect an existing RL Baselines3 Zoo model folder from an installed rl_zoo3 package.

Route here for

• Building no-render local evaluation commands with python -m rl_zoo3.enjoy or rl_zoo3 enjoy.
• Choosing the final model, best_model.zip, a specific rl_model_<steps>_steps.zip, or the latest checkpoint.
• Explaining --exp-id behavior, including --exp-id 0 as “latest numeric run”.
• Inspecting saved artifacts such as args.yml, config.yml, env_kwargs.yml, reward logs, checkpoint files, and vecnormalize.pkl.
• Planning a local benchmark smoke command with --test-mode --no-hub.

Route elsewhere

• To create or resume training artifacts, use the training-cli sub-skill.
• For live Hugging Face Hub download/upload, model cards, W&B, or video creation, use the integrations-hub-tracking sub-skill.
• For plotting or interpreting learning/evaluation curves, use the plotting-benchmarking sub-skill.

Default operating stance

1. Prefer installed-package commands. Use python -m rl_zoo3.enjoy when the rl_zoo3 console wrapper or optional plotting dependencies are questionable; otherwise rl_zoo3 enjoy is equivalent for the enjoy entry point.
2. Add --no-render for headless, CI, notebook server, or SSH sessions unless the user explicitly wants a live window.
3. Inspect the local artifact tree before assuming a model exists. The bundled inspector does not import rl_zoo3, does not load model weights, and performs no training.
4. Avoid relying on a local rl-trained-agents checkout or submodule. If a command would trigger Hub fallback, either route to the Hub integration sub-skill or add the local/offline guard described in the troubleshooting reference.

Bundled references and helper

• Start with evaluation workflows for command templates and selector semantics.
• Use artifact layout before debugging missing files.
• Use troubleshooting for common errors and fixes.
• Use scripts/model_artifact_inspector.py --help to inspect a model/log folder without loading weights.

Minimal workflow

python scripts/model_artifact_inspector.py \
  --folder logs --algo ppo --env CartPole-v1 --exp-id 0

python -m rl_zoo3.enjoy \
  --algo ppo --env CartPole-v1 -f logs --exp-id 0 \
  --no-render -n 1000

If inspection reports a missing selected model, do not “fix” it by training here; hand off artifact creation to the training-cli sub-skill.