8f6780dd3d55335216bab0f1cb397d682112f5f1
Mechanism-level analysis of the slot_aware_playability iter 240 plateau (opened_colors 4.95+, bad_open_rate 88-92%, calibration gap 6-9 → 2-4). Captures four expert consultations with diagnostic hypotheses, intervention catalog (architectural / training-dynamics / game-specific), measurement plan, and a comparison table across the four sources. Key new directions surfaced: - Current vs average vs league policy separation (Deep CFR average strategy is the convergence target, not advantage current). - All-negative fallback as Deep CFR ablation lever. - Empirical r̃ partitioning by action class. - Tabular Lost Cities oracle as a clean test of "is 5-color the game-theoretic answer or an approximation artifact". - Entry-gate target defined from traversal counterfactual values instead of heuristic labels. ideas.md gets an "Active Research Threads" pointer. 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.
Languages
Python
73.3%
Cython
21.7%
Julia
4.8%
Shell
0.2%