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>

configs/ismcts/default.yaml

@@ -24,6 +24,8 @@ mcts:
   n_simulations: 50
   c_puct: 1.5
   max_depth: 200
+  parallel_simulations: 8
+  virtual_loss_value: 1.0
 temperature:
   training: 1.0
   eval: 0.0
@@ -32,6 +34,8 @@ training:
   gradient_steps_per_iter: 10
   batch_size: 128
   replay_capacity: 100000
+  interleave_games: 8
+  interleave_max_batch: 64
 optimization:
   learning_rate: 0.0003
   grad_clip: 5.0

configs/ismcts/mini.yaml

@@ -24,6 +24,8 @@ mcts:
   n_simulations: 50
   c_puct: 1.5
   max_depth: 100
+  parallel_simulations: 8
+  virtual_loss_value: 1.0
 temperature:
   training: 1.0
   eval: 0.0
@@ -32,6 +34,8 @@ training:
   gradient_steps_per_iter: 10
   batch_size: 128
   replay_capacity: 50000
+  interleave_games: 8
+  interleave_max_batch: 64
 optimization:
   learning_rate: 0.001
   grad_clip: 5.0