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name: sf_transfer_recovery description: Run a bounded successor-features plus GPI transfer recovery experiment for changing linear-reward RL tasks.

Successor-Feature Transfer Recovery Harness

Use this skill to validate a reduced, mechanism-faithful recovery of Successor Features for Transfer in Reinforcement Learning. It should be used after successor-feature and GPI utilities exist and before claiming the paper mechanism has been recovered.

Inputs

• Attempt directory containing module_plan.json and environment/runtime_handoff.json.
• Generated skill root containing successor_feature_model and generalized_policy_improvement.
• Soft-mode permission for a reduced/proxy experiment when full four-room or MuJoCo recovery is not feasible.

Outputs

• recovery/recovery_result.json with mean_transfer_advantage.
• recovery/logs/generated_data_item.json describing the gridworld and reward weights.
• recovery/logs/generated_skill_invocations.json proving generated modules were imported/called.
• recovery/logs/training_trace.json recording exact dynamic-programming/source-policy construction traces.

Workflow

1. Construct a small deterministic shared-dynamics gridworld with feature-bearing terminal goals.
2. Define source reward weights and held-out transfer weights.
3. Derive source policies using exact value iteration for each source reward.
4. Compute successor features for each source policy using the generated successor-feature module.
5. Reweight source successor features for each held-out reward and apply generated GPI selection.
6. Evaluate GPI and a restricted single-source baseline on each held-out reward.
7. Save numeric returns, mechanism checks, and validation-ready artifacts.

Validation

Run:

python scripts/run_sf_transfer_recovery.py --attempt-dir <attempt_dir> --skills-root <generated_skills_root>
python tests/test_recovery_harness.py

Limitations

This is a reduced/proxy recovery. It does not reproduce the paper's four-room continuous-navigation or MuJoCo experiments, but it preserves the core mechanism of shared dynamics, changing linear rewards, successor-feature reweighting, and GPI transfer.