Weaning Schedule

When To Use

Use this skill when a PVRL or QDagger implementation needs the current coefficient on teacher-policy distillation. It is specifically for methods that should reduce teacher dependence over training. Do not use a fixed positive coefficient when the experiment claims QDagger-style weaning.

Inputs

• lambda_0: initial nonnegative teacher-distillation coefficient.
• Linear schedule inputs: step and decay_steps.
• Performance schedule inputs: student_score and teacher_score.

Outputs

• lambda_t: current nonnegative coefficient.
• progress: clipped progress or performance ratio.
• schedule: selected schedule type.

Workflow

1. Choose linear_decay for step-based experiments or performance_decay when student and teacher scores are available.
2. Clip progress to [0, 1].
3. Return lambda_0 * (1 - progress).
4. Record the schedule diagnostics in recovery traces.

Validation

Run:

python scripts/weaning_schedule.py --self-test
python tests/test_weaning_schedule.py

Limitations

The skill only computes coefficients. It does not decide when a teacher query is available or evaluate the RL policy.