Add multi-process self-play, eval workers, MCTS Cython port

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>

src/coolrl_lost_cities/games/classic/ismcts/config.py

@@ -25,14 +25,26 @@ class MctsConfig(StrictModel):
     c_puct: float = 1.5
     max_depth: int = 200
     use_rollout_value: bool = True
+    rollout_policy: str = "random"
+    parallel_simulations: int = 8
+    virtual_loss_value: float = 1.0
+    eval_with_mcts: bool = True
+    eval_n_simulations: int = 0
 
-    @field_validator("n_simulations", "max_depth")
+    @field_validator("n_simulations", "max_depth", "parallel_simulations")
     @classmethod
     def _positive_int(cls, value: int) -> int:
         if value <= 0:
             raise ValueError("must be positive")
         return value
 
+    @field_validator("rollout_policy")
+    @classmethod
+    def _rollout_policy(cls, value: str) -> str:
+        if value not in {"random", "heuristic_balanced"}:
+            raise ValueError("rollout_policy must be 'random' or 'heuristic_balanced'")
+        return value
+
 
 class TemperatureConfig(StrictModel):
     training: float = 1.0
@@ -44,8 +56,20 @@ class TrainingConfig(StrictModel):
     gradient_steps_per_iter: int = 10
     batch_size: int = 128
     replay_capacity: int = 100_000
+    interleave_games: int = 8
+    interleave_max_batch: int = 64
+    num_workers: int = 1
+    worker_device: str = "cpu"
 
-    @field_validator("games_per_iter", "gradient_steps_per_iter", "batch_size", "replay_capacity")
+    @field_validator(
+        "games_per_iter",
+        "gradient_steps_per_iter",
+        "batch_size",
+        "replay_capacity",
+        "interleave_games",
+        "interleave_max_batch",
+        "num_workers",
+    )
     @classmethod
     def _positive_int(cls, value: int) -> int:
         if value <= 0: