coolguy
be0c1a8d62
Diagnosis: mcts/value_prediction_error stuck at 300-1000 (RMSE ~22 on score range ±100), while loss/value stays at 0.05 because the loss divides prediction and target by value_scale=100 (so MSE / 10000). Net effect: the value head receives a tiny gradient relative to the policy head's ~1.75 cross-entropy loss, so it never learns to predict score scale well. This adds a config knob to multiply the normalized value loss without re-engineering the loss formula. value_loss_weight=50 recovers the raw MSE magnitude (~2.5 vs policy loss ~1.75), giving the value head comparable gradient signal.
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.