coolguy
169d4dcb14
50-iter sweep on default.yaml with stronger MCTS exploration: - c_puct: 3.0 -> 5.0 (UCB weight, more exploration of low-prior actions) - root_dirichlet_epsilon: 0.25 -> 0.4 (more noise injected at root prior) Standalone eval at iter 50 (30 games/opponent, all natural-end, timeouts=0): - vs heuristic-balanced: W=0/30 S=-70.5 (PA 0.15) - vs heuristic-aggressive: W=2/30 S=-65.1 (PA 0.14) [+10, +4] - vs heuristic-cautious: W=1/30 S=-48.0 (PA 0.14) [+2] 3 natural-end wins vs prev trapfix baseline iter 44 (which had 0 natural wins + 1 timeout-tie). Stall trap fixed remains true (timeouts=0 in c1). Trade-off observed: more exploration -> higher variance. Score avg vs cautious worsened (-32 -> -48), but win events appeared. For the non-terminal-win objective, exploration win > score-avg loss. Next: commit to long run (300 iter) with these params before tuning more.
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.