coolguy
b578b38628
맥락: 클래식 포트를 Python/Cython 구현과 pygame GUI 중심으로 정리한다. 변경: Rust 크레이트, proto 계약, Rust backend shim, 패리티 테스트를 삭제하고 문서와 backend factory를 Python 전용으로 갱신했다. 확인: uv run pre-commit run --all-files; uv run pytest tests/games/classic; uv run lost-cities-classic-gui --help
82 lines
2.3 KiB
Markdown
82 lines
2.3 KiB
Markdown
# Classic Port Notes
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This repository starts as a focused extraction of the Lost Cities game from the
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legacy `coolrl` repository. The first target is the classic two-player card
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game, without the earlier training-oriented tiers.
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## Current Direction
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- Implement the classic Lost Cities rules first.
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- Treat classic as the initial concrete game under `coolrl_lost_cities.games`.
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- Do not carry over `tier0` through `tier3`; those were useful for experiments,
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but they should not shape the first public game API.
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- Use the in-process Python/Cython implementation for the current port.
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- Keep the GUI in scope for local play, but do not carry a separate native backend.
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- Keep RL and training code out of the first extraction.
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The expected package shape is roughly:
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```text
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src/coolrl_lost_cities/
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games/
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classic/
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game.pyx
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env.py
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interfaces.py
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backends/
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bots/
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pygame_pvp.py
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fixtures/
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assets/
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docs/
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```
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Tests should live outside the package, roughly under:
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```text
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tests/games/classic/
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```
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## Out Of Scope For The First Port
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- Deep CFR
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- General training infrastructure
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- Evaluation loops for learned policies
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- Separate native backend support
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- Web client
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- Legacy experiment configs, checkpoints, logs, exports, and analysis artifacts
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Bot-vs-bot helpers can stay with the classic game if they are useful for smoke
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tests and local play. Broader policy evaluation can be introduced later with the
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training layer.
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## Later Training Shape
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If training is added later, it should not make Deep CFR the center of the
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package. Evaluation and policy interfaces should be general enough for multiple
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approaches, with Deep CFR as one implementation.
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A possible future shape:
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```text
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src/coolrl_lost_cities/
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games/
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classic/
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training/
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policies.py
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evaluation.py
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deep_cfr/
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imitation/
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policy_gradient/
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```
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The game package should expose rules, state transitions, legal actions, scoring,
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and playable UI. Training code can adapt those pieces later.
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## Naming Notes
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For now, use `classic` for the five-expedition game. Other variants, such as a
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six-expedition version, can be added later if needed. The current port should
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avoid adding a variant registry or broad abstraction before there is a second
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concrete game to support.
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