coolguyandClaude Opus 4.7
bac630b50b
Apply consultant + review recommendations to address training-budget shortfall and underutilized weighting: - traversal.traversals_per_player: 70 → 280 (4× sample touches/iter to reduce regret estimate variance early) - optimization.learning_rate: 3e-5 → 1e-4 (was too low for the 512×1024 updates schedule) - training_weighting.mode: none → lcfr (faster convergence; alpha/beta/ gamma fields are inert with mode=none) - evaluation.eval_every: 5 → 25 (eval was costing more wall-clock than training; 6 opponents × 100 games × 200 evals adds up) - Drop accidental duplicate keys in traversal/optimization sections (YAML last-wins, harmless but confusing) Wall-clock estimate ~13h on the existing setup. If results clearly improve, consider 8× traversals (560) as a follow-up. 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.