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name: jsrl_guide_policy_contract description: Validate a Jump-Start Reinforcement Learning guide-policy interface and better-than-random progress assumptions before using it for roll-in.

JSRL Guide Policy Contract

When To Use

Use this skill when a recovery or implementation needs to decide whether a prior policy is suitable as the fixed JSRL guide-policy. It is appropriate for scripted, imitation-trained, offline-RL, or manually engineered guides. Do not use it to train the guide; it only wraps and validates one.

Inputs

• A callable or table-like guide-policy that maps observations/states to actions.
• An environment or small deterministic probe exposing legal actions and progress-to-goal information.
• Optional baseline/random policy for comparison.

Outputs

• Validated action selections from act.
• A guide quality report with success count, average progress, and useful: true/false.
• Warnings for stationary, invalid-action, or adversarial guides.

Workflow

1. Wrap the guide behind a minimal act(state) function.
2. Check every returned action against the environment's legal actions.
3. Run bounded rollouts and compare progress or success against a baseline.
4. Treat a guide as useful only if it improves progress or success enough to create good handoff states.
5. Record limitations when the guide is related-task, stochastic, or suboptimal.

Validation

Run:

python scripts/guide_policy_contract.py --demo
python -m pytest tests

The script is standard-library only and can also be imported by recovery harnesses.

Limitations

This skill does not prove the guide is optimal. It only checks the paper's minimum JSRL assumption: the guide can act from observations and is better than a random or stationary baseline on a bounded probe.