coolguy
6c976f468a
Adds torch.autocast(fp16) + GradScaler around _train_advantage and _train_strategy when run.use_amp=true and device=cuda. CPU/non-CUDA falls back to fp32 no-op. Mitigations: - scaler.unscale_(optimizer) before grad_clip. - nonfinite-loss guard skips overflowing batches and counts them. - diff.float().square() in advantage loss to avoid fp16 overflow. - strategy mask/log_softmax kept in fp32. New metrics: amp/grad_scale, amp/nonfinite_loss_count. Tests: AMP CUDA smoke + CPU fallback in test_deep_cfr_trainer.py. Bench: scripts/bench_amp_trainer.py micro-benches train phases under synthetic replay memory. smoke.yaml result is fp32 3.22ms / AMP 3.92ms (0.82×, regression). 100-iter A/B on default.yaml deliberately skipped: smoke regression mirrors the 2026-05-07 torch.compile regression dynamic (dispatch overhead > kernel benefit at this model size) and re-confirming on the same size adds no information. Default stays run.use_amp: false. Re-enable trigger documented in docs/performance.md: hidden_size >= 1024 or num_layers >= 6, then run the bench script + 100-iter A/B before flipping default.
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.