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name: opal_offline_segment_protocol description: Prepare fixed-horizon offline state-action segments for OPAL primitive discovery and downstream latent relabeling.

OPAL Offline Segment Protocol

When To Use

Use this skill when an OPAL-style recovery needs to transform offline trajectories into fixed-horizon primitive-learning segments. Do not use it to train the primitive model or decide high-level latent actions.

Inputs

• JSON trajectories with states, actions, and optional rewards.
• Primitive horizon c greater than zero.
• A segmentation mode; the bundled script implements deterministic non-overlapping windows.

Outputs

• Segment records with trajectory_id, start, horizon, states, actions, initial_state, and optional reward summaries.
• A summary containing segment count and dropped short tails.

Workflow

1. Validate that each trajectory has enough actions and states.
2. Slice non-overlapping windows of length c.
3. Preserve the first state as s0 for the OPAL prior.
4. Keep reward metadata separate from primitive segment content.
5. Report dropped tails rather than padding silently.

Validation

Run:

python tests/test_segment_protocol.py

Limitations

The script is intentionally small and deterministic. It does not implement overlapping windows, padding, D4RL loading, or neural preprocessing.