Proxy Recovery Harness

When To Use

Use this skill when full Atari-scale reincarnating RL is blocked by runtime, checkpoint, or data availability, and soft-mode reduced recovery is permitted. The harness validates QDagger's mechanism with a deterministic tabular proxy. Do not present its metric as a full Atari result.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skills root containing qdagger_loss and weaning_schedule.
• Optional output directory for recovery artifacts.

Outputs

• generated_data_item.json: proxy replay and teacher-policy data.
• training_trace.json: loss, parameter, schedule, and optimizer-step evidence.
• recovery_result.json: proxy metric and mechanism checks.
• generated_skill_invocations.json: evidence that generated module skills were imported/called.

Workflow

1. Load the generated QDagger loss and weaning schedule helpers by path.
2. Construct a tiny teacher replay and student replay batch.
3. Compute pre-update loss with positive teacher coefficient.
4. Estimate finite-difference gradients over tabular Q-values and apply one optimizer step.
5. Recompute post-update loss and a later decayed coefficient.
6. Write validator-compatible recovery artifacts.

Validation

Run:

python scripts/run_proxy_recovery.py --self-test
python tests/test_proxy_recovery_harness.py

Limitations

The harness is a reduced mechanism-faithful proxy. It proves executable QDagger mechanics but not paper-scale ALE performance.