9696282110
Capture all successful state reads with hashes and session-local IDs. Record action intent before POST, retain accepted/rejected/unknown results, and link subsequent observations. Preserve legacy feeds and finalize each invocation synchronously.
70 lines
3.3 KiB
Markdown
70 lines
3.3 KiB
Markdown
# Policy structure
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## Current boundaries
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```text
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run.py observe, execute, report results
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recording.py linked observations, proposals, attempts, and results
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run_state.py reported run identity and scoped combat-pile evidence
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brain.py entry point, dispatch, navigation, shops, minigames
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policy/
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__init__.py package marker; no registration or initialization
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context.py Decision, PendingAction, PolicyContext
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combat.py combat proposals and model questions
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selection.py card, relic, bundle, hand, and reward proposals
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facts.py observation parsing and combat calculations
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```
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`brain.decide()` remains the policy entry point. The runner owns a `RunContext`,
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which holds policy memory and replaces it on reported run changes. The runner
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reports action results through that context.
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`brain.Decision` and `brain.PolicyContext` remain available as direct imports
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of the shared types, so the runner interface does not change.
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Policy modules may ask Jev for preferences. They must not call the game API.
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`context.py` does not call either service. Combat arithmetic remains in
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`facts.py`, not duplicated inside combat policy.
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The dependency direction is simple:
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- The runner uses `brain`.
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- `brain` uses the combat, selection, and context modules.
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- Combat and selection use context, facts, and the model client.
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- Context uses only the Python standard library.
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- No policy module imports `brain` or the runner.
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## State and configuration
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A proposal does not update execution memory. Accepted requests are reconciled
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with fresh observations. See [policy-state behavior and limits](research/13-policy-state.md)
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for the evidence required by each guarded action.
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The existing card-skip setting stays in `brain.CARD_SKIP_POLICY`. The dispatcher
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passes its value explicitly to `selection.card_reward_decision(skip_policy=...)`.
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This avoids a circular import and preserves the existing command-line behavior.
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It is configuration, not per-screen action memory.
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## Why stop here
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These are plain modules, not a plugin framework or class hierarchy. Navigation,
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shops, and minigames stay together until a further split helps development.
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The extraction does not change game decisions, confidence gates, fallbacks,
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state reconciliation, or recording. The subsequent
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[run-state pass](RUN_STATE.md) adds reported identity and card-evidence provenance.
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[Linked recordings](RECORDING.md) provide the corresponding session journal.
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## Extraction verification
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- 262 existing assertions passed. No test files or assertions were added for the extraction.
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- Thirteen isolated whole-process scenarios passed with local HTTP fixtures.
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- All 346 stored observations produced identical decisions before and after extraction,
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in both model-free mode and local-model-stub mode: 692 comparisons.
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- The local model requests also matched, including their state and questions.
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- Thirty-six function/class definitions retained identical abstract syntax trees.
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Only the two dispatch functions and explicit card-skip parameter changed.
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- Dataset integrity, Python compilation, and shell syntax checks passed.
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The differential and whole-process probes were temporary. They made no live
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game or paid model calls. These checks establish refactoring equivalence for
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the tested inputs, not correctness for every possible game state.
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