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name: sac_update_step description: Execute one deterministic reduced Soft Actor-Critic critic actor and target update for recovery evidence.

SAC Update Step

Use this skill for bounded recovery experiments that must show the SAC update mechanism without full MuJoCo or neural-network training. It consumes a validated replay batch and scalar parameters, then emits losses, changed parameters, and mechanism flags.

Inputs

• Replay batch with transition dictionaries.
• Scalar parameters value, q1, q2, policy, and target_value.
• Hyperparameters gamma, alpha, lr, and tau.

Outputs

• loss_before and loss_after.
• params_before and params_after.
• Mechanism checks for entropy, replay use, twin Q, actor/critic update, target update, and optimizer change.

Workflow

1. Compute soft value targets using min(q1, q2) - alpha * log_prob.
2. Compute Q targets from reward plus discounted target value.
3. Compute actor pressure from alpha * log_prob - min(q1, q2).
4. Apply a small scalar gradient-like update to value, Q, and policy parameters.
5. Apply Polyak averaging to the target value parameter.

Validation

Run python tests/test_update.py or validate the full skill tree with the Distiller validator.

Limitations

This is a declared reduced/proxy update and must not be reported as full paper-scale SAC training.