coolguy
812dace1e3
- Loss section: add ISMCTS policy/value twin-axis panel alongside the Deep CFR advantage/strategy panel. Both render only when their keys exist; the unused side shows "No data". - Memory Size: add memory/replay alongside memory/advantage and memory/strategy. - New MCTS section (analysis_09_mcts.png): visit-count entropy, value prediction error, policy/MCTS KL — three iter-time scalars emitted by IsMctsTrainer. Auto-skips on Deep CFR runs (no data).
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.