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name: diayn_skill_prior_conditioning description: Build fixed-prior latent skill schedules and per-timestep conditioning records for DIAYN-style unsupervised rollouts.

DIAYN Skill Prior Conditioning

Use this skill when a recovery or implementation needs the DIAYN latent skill sampling contract: a fixed categorical prior, one sampled skill per episode, and the same skill used for every timestep in that episode. Do not use it for learned skill priors or task-reward meta-policies.

Inputs

• num_skills: positive integer number of categorical skills.
• episodes: positive integer rollout episode count.
• horizon: positive integer timestep count per episode.
• Optional seed for deterministic sampling.

Outputs

• Episode-level sampled skill ids.
• Timestep conditioning records with one-hot skill vectors.
• Uniform prior probabilities and log_prior values.

Workflow

1. Validate positive dimensions and create a uniform prior over skill ids.
2. Sample one skill id per episode with the supplied seed.
3. Expand each episode skill into all timesteps without resampling.
4. Pass log_prior to DIAYN reward computation as log p(z).
5. Preserve generated schedule logs as recovery evidence when running experiments.

Validation

Run python scripts/skill_prior.py --num-skills 3 --episodes 3 --horizon 2 --seed 7 for a JSON smoke output. Run python -m pytest tests or the Distiller skill-tree validator with --run-tests for deterministic assertions.

Limitations

The helper does not train a policy and does not infer a skill prior from data. It intentionally preserves the fixed-prior assumption from DIAYN.