coolguyandClaude Opus 4.7
b6863b3ba0
ColorSharedNetwork previously sliced the input vector into n_colors equal chunks (input_dim // n_colors). The slice boundaries do not align with the actual encoding layout: adjacent slices contain phase flags, hand slots, expedition state, scores, etc. mixed together. The "color-shared" encoder was therefore sharing weights across semantically unrelated chunks, not across per-color blocks. The single archived run that exercised this path (2026-05-07_092137_color_shared_attention_1000iter) was killed at iter 41 and produced no eval data, so we have no measurement of whether a real per-color architecture would help. Adds compute_lost_cities_color_layout(input_dim) which returns explicit per-color and common index lists for the standard Lost Cities encoding (n_colors=5, hand_size=8, n_ranks=9). It recognises input_dim values 171, 219, 249, 297 across derived_playability and slot_aware_playability flag combinations. Per-color block (39 dims when derived_playability is on): both players' expedition state for that color, discard top, public-histogram row, pending-discard one-hot bit, legal-action draw-pile bit, and the derived_playability per-color block. Slot-aware features are slot-major and stay in common. ColorSharedNetwork.forward now indexes per-color blocks via the layout when input_dim matches a known schema. For other dims (unit tests, non-Lost Cities use), it falls back to chunked slicing with a UserWarning - preserves backward compatibility for tests but makes the legacy behaviour visible. No fair test of the new architecture was run as part of this commit. Documented in docs/plans/deep-cfr-selectivity.md section 7. 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, 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.