coolguyandClaude Opus 4.7 f63c4b8059 Strip heuristic input features and add selectivity diagnostics
Audit of the Deep CFR information-state encoding identified two tiers of
non-pure features and removed both:

- Tier 3 (judgment): is_bad_open_candidate, open_risk_score,
  is_safe_continuation. Same heuristic family used to label bad_open in
  evaluation, embedded as model input.
- Tier 2 (projection): recoverable_score_no_bonus,
  recoverable_margin_no_bonus, min_needed_to_break_even,
  cards_needed_for_bonus, has_bonus_path. Mechanical but assumption-laden
  ("commit and play all currently-playable cards"). The no_bonus form is
  asymmetric: it amplifies the immediate -20 penalty while truncating the
  +20 bonus upside, biasing the model toward the same "don't open" basin
  the diagnostics already flagged.

Input dim 365 -> 297. DERIVED_PLAYABILITY_PER_COLOR 19 -> 15;
SLOT_AWARE_PLAYABILITY_PER_SLOT 12 -> 6. Test shape assertions updated.

Also adds selectivity diagnostic infrastructure used to reach this point:
- traversal.outcome_unsampled_first_open_prior_alpha config field with
  signed-prior overlay on unsampled first-open advantage targets (A1).
- analyze_first_open_counterfactual.py --post-policy to swap the
  policy_player rollout policy and isolate selection bias (D1).
- analyze_first_open_followup.py to inspect post-forced-open behavior
  (E2): same-color play vs discard counts, other-open rate, terminal
  hand composition.

Findings recorded in docs/plans/deep-cfr-selectivity.md sections 3-6.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-10 14:26:16 +09:00
2026-05-07 20:41:27 +09:00
2026-05-08 16:22:52 +09:00
2026-05-07 15:42:30 +09:00

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.

S
Description
Lost Cities
Readme
7.7 MiB
Languages
Python 73.3%
Cython 21.7%
Julia 4.8%
Shell 0.2%