coolguy
b43bb5917f
맥락: - GUI 이식 전에 Rust crate와 proto schema 위치를 Python package 내부에서 분리한다. - Cargo 작업, IDE 인식, 빌드 산출물 관리를 루트 구조에 맞춘다. 변경: - rust_core를 rust/lost-cities-core로 이동하고 proto/lost_cities.proto를 루트 proto 디렉터리로 옮겼다. - Rust build.rs, Python Rust backend, Rust parity 테스트의 경로를 새 위치로 수정했다. - Python package-data에서 Rust crate와 proto 항목을 제거하고 README/port notes를 갱신했다. 확인: - uv sync --extra gui --reinstall-package coolrl-lost-cities - uv run pytest tests/games/classic - uv run lost-cities-classic
86 lines
2.3 KiB
Markdown
86 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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- Keep backend selection available. The Python/Cython and Rust implementations
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should remain swappable behind a small backend boundary.
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- Keep the GUI and Rust implementation in scope for the port.
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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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rust/
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lost-cities-core/
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proto/
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lost_cities.proto
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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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- 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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backend selection, 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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