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name: diayn_discriminator_intrinsic_reward description: Compute DIAYN discriminator log-probabilities, cross-entropy diagnostics, and intrinsic rewards from skill-conditioned state predictions.

DIAYN Discriminator Intrinsic Reward

Use this skill when implementing or validating the DIAYN reward term log q_phi(z|s) - log p(z). It is appropriate for tiny deterministic fixtures, reduced recovery experiments, and full implementations that need stable reward diagnostics. Do not use it to add downstream task rewards.

Inputs

• A batch of discriminator logits or scores shaped as batch x num_skills.
• True sampled skill ids for each row.
• A fixed log_prior, usually -log(num_skills) for a uniform prior.

Outputs

• Stable log-softmax values.
• Selected log q_phi(z|s) values.
• Intrinsic reward values.
• Mean reward, cross-entropy loss, and accuracy.

Workflow

1. Validate batch and label dimensions.
2. Convert logits to log probabilities with a max-subtracted log-softmax.
3. Select log probability for the true skill on each transition.
4. Compute pseudo-reward by subtracting log_prior.
5. Report loss and accuracy for discriminator training diagnostics.

Validation

Run python scripts/diayn_reward.py --demo. Tests verify that confident correct logits produce higher rewards and valid discriminator metrics.

Limitations

This skill computes the reward and discriminator diagnostics only. It does not update neural network weights; pair it with a policy or discriminator optimizer in a larger recovery harness.