coolguyandClaude Opus 4.7
f289997c1c
Trap diagnosis: agent learned to stall (avoid opening expeditions, draw from discard pile to extend deck) until max_steps timeout, then squeak by on opponents' negative scores. All eval wins were from timeouts; agent never won a naturally-terminating game. Self-play reinforced this because timeout games still got a positive value target. Fixes (no algorithm change, all MCTS hyperparameters or signal shaping): - Dirichlet noise at root prior (AlphaZero standard, was missing): mcts.pyx `_expand_with_prior` takes `is_root` flag; root expansion mixes prior with Dirichlet(α). Callers in interleaved_self_play and the internal evaluate_and_backup pass `not item.path`. - Default config strengthens exploration on the 50-sim batched search: c_puct 1.5 -> 3.0, virtual_loss_value 1.0 -> 5.0, plus new root_dirichlet_alpha=0.3 / root_dirichlet_epsilon=0.25. - Self-play timeout signal zeroed: `_finalize_context` sets v_target=0 if context.state is not terminal. Stops the network from learning "stall = positive value". New standalone evaluator: - `lost-cities-ismcts eval` subcommand (eval_checkpoint.py): loads a checkpoint, runs N games per opponent across a parallel pool, reports win/score with 95% CIs plus per-game logging via --verbose. Defaults cover heuristic-balanced/aggressive/cautious (rollout policy isn't in the training-eval opponent list, so this is the natural way to compare the trained policy against its rollout target). Tests (19) still pass; .so rebuilt. 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.