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name: stochastic_actor_update description: Run a deterministic SAC-style stochastic actor update with reparameterized Gaussian action, log probability, and policy-gradient diagnostics.

Stochastic Actor Update

Use this skill when a recovery needs to exercise the actor component of SAC without depending on a full deep-learning stack. It implements a tiny scalar Gaussian actor with fixed noise so the reparameterized policy loss can be audited deterministically.

Inputs

• Actor mean and log standard deviation parameters.
• State feature and deterministic noise sample.
• A differentiable quadratic Q proxy target.
• Learning rate.

Outputs

• Action, log probability, Q value, policy loss.
• Updated actor parameters and parameter-change evidence.

Workflow

1. Reparameterize the action as mean * state + exp(log_std) * noise.
2. Evaluate Gaussian log probability for the noise-scaled action.
3. Compute SAC policy loss alpha * log_prob - Q(action).
4. Use analytic gradients for the scalar actor and update parameters.
5. Record before/after parameters for validation.

Validation

Run python tests/test_actor_update.py or the Distiller skill validator.

Limitations

This is a scalar deterministic proxy for the SAC actor update. It preserves the optimization contract but is not a neural-network policy implementation.