coolguy
cba6caee2f
C13/C14 cycles: heuristic-balanced bot plays a configurable fraction of self-play games (training.mixed_opponent_fraction). Trainee turns are stored as policy samples; opponent turns are taken by the bot directly and not stored. When mcts.opponent_aware_search is set, the MCTS tree also treats the opponent seat as that bot — opponent moves are applied without expanding into the search tree, and all values are taken from the traverser's perspective. This was Codex's top recommendation for breaking the symmetric self-play weak fixed point. Empirical: opponent-aware mixed self-play does NOT lift win-rate above BC pretrain (vs heuristic-cautious 100-game eval): C13 (mixed=0.5, no KL): 0/100 — catastrophic forgetting C14 (mixed=0.2, KL beta=1): 17/100 — preserved BC, no improvement BC pretrain baseline: 21/100 Combined with C10-C12 results, BC remains the ceiling under our compute budget (1 GPU + 50 sims + 768x4 net). Code is left in place as configurable dials for future runs with more compute.
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.