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name: transition_skill_batching description: Build and validate aligned CIC state-transition and continuous skill batches for contrastive intrinsic control recovery experiments.

Transition Skill Batching

Use this skill when a CIC-style experiment needs to construct tau = concat(s, s_next) and preserve alignment between transition tuples and continuous skill vectors. Do not use it for downstream reward evaluation or for contrastive loss computation; those belong to separate skills.

Inputs

• states: two-dimensional numeric rows representing current states.
• next_states: two-dimensional numeric rows with the same shape as states.
• skills: two-dimensional numeric rows with the same batch size.
• Optional synthetic batch arguments: batch_size, state_dim, skill_dim, and seed.

Outputs

• tau: transition tuples formed by concatenating each state and next state.
• metadata: batch size, dimensions, and source label.
• JSON output when running the CLI script.

Workflow

1. Validate all arrays are non-empty rectangular numeric matrices.
2. Confirm states and next_states have identical shapes.
3. Confirm skills shares the same batch size.
4. Concatenate current and next states across feature dimension.
5. For reduced recovery, generate a deterministic synthetic batch where each skill controls a repeatable transition direction.

Validation

Run:

python scripts/transition_batch.py --demo
python tests/test_transition_batch.py

Limitations

This skill does not estimate rewards, train encoders, or sample from an RL replay buffer. It only preserves the transition-skill data contract needed by CIC.