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name: opal_primitive_autoencoding_objective description: Train or check an OPAL-style primitive autoencoding objective with reconstruction loss and KL-style prior matching.

OPAL Primitive Autoencoding Objective

When To Use

Use this skill when a recovery needs to learn latent primitive summaries from fixed-horizon offline segments. It is appropriate for full neural training or a declared reduced proxy that preserves reconstruction, latent assignment, and prior matching.

Inputs

• Segment JSON produced by opal_offline_segment_protocol.
• Number of latent primitives for reduced recovery.
• Beta/KL weight and update count.

Outputs

• Reconstruction loss before and after updates.
• Prior/KL-style penalty before and after updates.
• Segment latent assignments.
• Decoder prototype parameters before and after updates.

Workflow

1. Load fixed-horizon state-action segments.
2. Infer a latent assignment from each segment's action pattern.
3. Decode actions from latent-conditioned prototype parameters.
4. Compute mean squared reconstruction loss and a prior-matching penalty from initial state to latent assignment.
5. Apply real parameter updates to decoder prototypes.
6. Report latent separation and optimizer/update evidence.

Validation

Run:

python tests/test_primitive_objective.py

Limitations

The bundled script is a reduced deterministic proxy, not a full neural beta-VAE. It must be labeled as reduced recovery when used as primary experiment evidence.