coolguyandClaude Haiku 4.5
d0316293e4
opponent_policy=network 설정으로 1000-iter 실험 두 개 (512x3, 1024x4)를 돌린 결과 두 실험 모두 policy collapse가 발생함을 확인. 큰 capacity는 plateau를 늘리지만 발산 자체를 막지 못함. 향후 학습은 self_play_league 기본값을 유지할 것을 권고. - 실험에 사용한 config 두 개 추가 - 발견 분석 문서 추가 (원인, 비교, 권고) Co-Authored-By: Claude Haiku 4.5 <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.