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name: rnd_observation_normalization description: Normalize observations for Random Network Distillation with running statistics and clipping before target and predictor feature computation.

RND Observation Normalization

Use this skill when implementing or validating Random Network Distillation (RND) pipelines that feed observations into a frozen random target and a trainable predictor. Do not use it as a generic image augmentation skill; its purpose is to preserve the paper's scale-control mechanism.

Inputs

• A batch of numeric observations represented as lists of equal-length vectors.
• Optional running statistics with mean, var, and count fields.
• Optional clipping bounds, defaulting to the paper's [-5, 5] range.

Outputs

• Normalized and clipped observation vectors.
• Updated running statistics that must be shared by target and predictor paths.

Workflow

1. Merge incoming batch moments with existing running moments using a numerically stable parallel variance update.
2. Normalize each dimension with (x - mean) / sqrt(var + eps).
3. Clip normalized values after whitening.
4. Feed exactly the same normalized batch to both RND target and predictor modules.

Validation

Run python scripts/normalization.py --self-test or validate the skill tree with tests enabled. The self-test checks running-stat updates, finite normalization, and clipping bounds.

Limitations

The script handles vector observations for deterministic recovery and tests. Image tensors should be flattened or batched into vectors before use, while preserving the same running-stat semantics.