coolguyandClaude Opus 4.7
2429d1e210
진단 가설/개입/측정 섹션은 docs/research/lost_cities_selectivity.md 에 verbatim으로 보존되어 있어 ideas.md 쪽 사본은 stale 위험 + 중복 유지 비용만 남는다. ideas.md는 brainstorm 인덱스로 축소하고, research/ 의 세 thread를 단일 진입점으로 정리. 기존의 "All-negative fallback 가설 — 검증됨, 부분 풀림" 같은 stale 표현도 함께 사라진다 (research doc에선 여전히 open hypothesis로 다뤄지고 있음). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
coolrl-lost-cities
Focused Lost Cities extraction from the legacy coolrl repository.
The current implementation starts with the classic two-player card game:
- classic 5-expedition rules by default
- Python/Cython game engine
- env wrapper
- random, passive-discard, and safe-heuristic bots
- core rule, scoring, mask, env, canonical-state, bot, and GUI smoke tests
Training code, Deep CFR, learned-policy evaluation, GUI, and web client are intentionally outside the first port.
Development
uv run pytest tests/games/classic
uv run lost-cities-classic
For future GUI work, install the optional GUI dependencies:
uv sync --extra gui
Run the classic pygame GUI:
uv run lost-cities-classic-gui --mode pvc --bot safe-heuristic
The GUI uses the in-process Cython game engine.
Basic Usage
from coolrl_lost_cities.games.classic import GameState, build_bot, classic_config
state = GameState.new_game(classic_config(seed=1))
bot = build_bot("random", seed=1)
while not state.terminal:
state.apply_action(bot.act(state))
print(state.total_score(0), state.total_score(1))
See classic port notes for the current direction.