coolguy
200129d16d
Codex flagged that mcts/value_prediction_error mathematically reconciles with loss/value (MSE / value_scale^2 = 0.05) but the latter looks healthy while the former says the value head is far off. To make this clearer in W&B, expose: - mcts/value_rmse — sqrt(MSE), in raw score units (interpretable) - mcts/v_target_abs_mean — magnitude of |v_target|, indicates if game outcomes are very lopsided (always negative for a losing agent) - mcts/v_target_std — spread, low std means targets are saturated to one end (e.g., always -100ish) These let us see whether value head is failing because targets are unlearnable variance, or just hard-to-predict, or because of saturation at the value_scale=100 tanh boundary. Tests: 19/19 passing.
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, discard-only, 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.