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name: opal_hierarchical_latent_control description: Relabel downstream data with OPAL primitive latents and evaluate high-level latent control over temporally extended actions.

OPAL Hierarchical Latent Control

When To Use

Use this skill after primitive latents and decoder parameters are available. It tests the OPAL downstream interface: a high-level policy chooses a latent every c steps and the low-level primitive decoder supplies primitive actions.

Inputs

• Segment JSON and primitive objective output with latent assignments and decoder prototypes.
• A sparse proxy goal or reward-labeled downstream data.
• Primitive horizon c.

Outputs

• Latent-labeled high-level dataset summary.
• Rollout trace with latent decisions and decoded primitive actions.
• Success rate and effective horizon.

Workflow

1. Relabel each segment using encoder/assignment output.
2. Build a high-level action table in latent space.
3. Select latents that move toward the goal while staying among learned primitive labels.
4. Decode each latent for c primitive steps.
5. Report success and compare latent decisions with primitive action count.

Validation

Run:

python tests/test_hierarchical_control.py

Limitations

The bundled controller is deterministic and intended for reduced proxy recovery. Full offline RL algorithms such as CQL must be integrated separately when available.