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name: evaluation-and-persistence description: "Evaluate Stable-Baselines3 policies, configure callbacks/logging, save/load models and auxiliary state, transfer parameters, and debug portability issues." disable-model-invocation: true metadata: disco-role: operating license: MIT

Evaluation and Persistence

Use this sub-skill when the task is about measuring policy quality, configuring evaluation/checkpoint callbacks, logging training/evaluation metrics, saving or loading SB3 artifacts, inspecting save files, transferring weights, or making loaded models portable across machines/devices.

Start here

• For evaluate_policy, EvalCallback, CheckpointCallback, BaseCallback, and frequency caveats, read evaluation-and-callbacks.md.
• For save(), load(), SB3 zip contents, custom_objects, print_system_info, replay buffers, VecNormalize, and parameter transfer, read save-load.md.
• For default logger behavior, TensorBoard, progress bars, custom scalar/image/video logging, and logger output names, read logging.md.
• For verified signatures and import paths, read api-reference.md.
• For common failure modes and portability triage, read troubleshooting.md.
• To evaluate an existing .zip model without bundled checkpoints, use scripts/evaluate_saved_model.py.

Boundaries

• Use other sub-skills for choosing algorithms/training hyperparameters, creating custom environments/vectorization stacks, or designing custom policies/networks.
• This sub-skill assumes the environment and algorithm choice are already known and focuses on evaluation, callbacks, logging, persistence, and replay/normalization state.