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name: fb_greedy_policy description: Extract no-planning greedy policies from FB forward embeddings and a projected reward vector.

FB Greedy Policy Extraction

Use this skill when z_R has already been estimated and a policy must be derived by direct FB scoring. Do not use it to plan, search, or update embeddings for the new reward.

Inputs

• Forward embeddings F indexed as state-action rows.
• Task vector z_R.
• Number of states and actions, with deterministic row order state * action_count + action.

Outputs

• Q-value table F(s,a,z_R)^T z_R.
• Greedy action for each state with deterministic lowest-index tie breaking.

Workflow

1. Compute dot products for all state-action rows.
2. Group scores by state and select the maximum action.
3. Record ties and avoid any environment planning or reward-specific optimization.
4. Hand the policy to recovery or evaluation modules.

Validation

Run python scripts/greedy_policy.py --self-test. Tests verify known best actions and deterministic tie handling.

Limitations

The script assumes finite action enumeration and precomputed forward embeddings.