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name: jsrl_switching_rollout_curriculum description: Build Jump-Start Reinforcement Learning rollouts that switch from guide-policy control to exploration-policy control under curriculum or random guide-step schedules.

JSRL Switching Rollout Curriculum

When To Use

Use this skill to collect trajectories for JSRL or to test whether a recovery harness correctly implements the paper's two-policy handoff. It is useful for full RL environments and for reduced sparse-reward proxy environments.

Inputs

• Environment with reset() and step(action).
• Guide-policy and exploration-policy callables.
• Horizon H, selected guide-step h, and optional guide-step schedule.
• Threshold β used to advance curriculum stages.

Outputs

• Trajectory records with state, action, reward, next state, done flag, and controller equal to guide or exploration.
• Handoff summary with selected h, guide action count, exploration action count, total reward, and success flag.
• Curriculum cursor updates for threshold-based advancement.

Workflow

1. Clamp the selected guide-step to the valid horizon.
2. Roll out the guide-policy while t < h.
3. Roll out the exploration-policy after the handoff until termination or horizon.
4. Append controller labels to every transition.
5. Advance the curriculum only after evaluation meets or exceeds β.
6. For ablations, sample guide steps from the same schedule without changing the data schema.

Validation

Run:

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

The demo prints a small trajectory and handoff summary.

Limitations

This skill does not implement a complete RL algorithm. It only owns the rollout and curriculum contract; policy/value updates belong in the value exploration update skill.