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>
Implements a proof-of-concept single-observer IS-MCTS trainer with AlphaZero-style policy/value network, determinization, replay, self-play, CLI configs, and focused tests. Mini acceptance run reaches positive random eval while keeping play_action_rate above the Deep CFR trap threshold.
Tests: uv run python -m pytest tests/games/classic/ismcts/ -x; uv run python -m pytest tests/games/classic/test_deep_cfr_trainer.py -x; uv run lost-cities-ismcts train --config configs/ismcts/mini.yaml
coolguyandClaude Opus 4.7