coolguyandClaude Opus 4.7
651175e5bd
Key changes for ISMCTS speed and correctness: - Cython port: HeuristicBot helpers (`heuristic_cy.pyx` + new `.pxd`) and ISMCTS searcher (`mcts.pyx`) now run as cdef. Both share a fast unified-action path through GameState's C interface to avoid Python round-trips on hot rollout/tree-walk paths. - Multi-process self-play and eval: `workers.py`, `eval_worker.py`, `interleaved_self_play.py`, plus trainer wiring with ProcessPoolExecutor + spawn context. Eval inside `evaluate.py` is parallel per opponent. - ISMCTS-specific eval (`evaluate.py`) runs MCTS at decision time so the metric matches deploy mode; `evaluation.eval_with_mcts` flag preserves backwards-compatible policy-only eval when needed. - Trainer logs progress per phase (self-play start/done, eval per opponent), and value loss is now scaled by `value_scale` so policy and value losses sit on comparable magnitudes. - Compact info-set key (`info_set.py`) using packed-struct format and child-key reuse during MCTS descent to cut per-step canonicalization. Tests: 19 ISMCTS suite passing, including parity (Cython-vs-Python sequential, batched-vs-sequential visit counts, push/pop round-trip). 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, 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.