coolguy
0d35341bbe
C4 (768x4 + rollout=True, 150 iter) result: 0/300 natural wins, score avg -85 to -97 vs three heuristic opponents. Bigger network alone did not produce wins; PA shifted up to 0.23-0.25 (similar to c3 without rollout) but agent still loses every natural-end game. Capacity hypothesis rejected: 2.5x more params (~2M vs ~800k) did not break the loss pattern. Score average actually slightly worse than 512x3 baseline. So the bottleneck is not network capacity. Going to the long-run experiment: AlphaZero-correct setup with the network value loop closed. use_rollout_value=false means leaf Q comes from network value head. Training signal: network value learns from actual game outcomes; MCTS uses those values to pick actions; better actions produce better outcomes; cycle closes. 100 iter previously gave essentially the same result as rollout=true (comparing c3 to trapfix baseline). Both are too early in the AlphaZero training curve. Standard AlphaZero papers train 1000s of iterations. Going long: 1000 iter with the current config. Self-play ~9s/iter without rollout, total wall ~150 min for the train phase. Plotting strategy: at iter 200, 500, 1000, run 100-game standalone eval and generate analyze.py plots to visualize trajectory. Network kept at 768x4 since bigger capacity does not actively hurt.
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.