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name: tabular_usfa_recovery description: Run a bounded Trip-MDP proxy experiment that validates USFA TD learning and GPI transfer mechanics.

Tabular USFA Recovery

Use this skill when full USFA navigation training is unavailable but soft-mode recovery permits a declared mechanism-faithful proxy. The experiment must still execute real TD updates, GPI candidate search, and metric computation.

Inputs

• Training task weights and candidate encodings.
• Test preference grid.
• Output directory for recovery artifacts.
• Paths to the generated USFA helper skills.

Outputs

• recovery_result.json with metric and mechanism checks.
• logs/training_trace.json with loss and parameter changes.
• logs/generated_data_item.json describing the Trip-MDP proxy item.
• logs/generated_skill_invocations.json showing helper skill usage.

Workflow

1. Build the paper's Trip-MDP-style feature table.
2. Initialize a tabular USFA table with trainable entries for sampled encodings.
3. Run vector TD updates using the linear-reward successor-feature helper.
4. Evaluate interpolated tasks with the GPI helper.
5. Save metrics and mechanism checks from the executable script.

Validation

Run:

python tests/test_trip_usfa.py

Limitations

The script is a reduced proxy, not a full 3D navigation reproduction. It is valid only when the run is in soft recovery mode and full runtime blockers are recorded.