Add Dirichlet root noise + standalone eval CLI, fix self-play stall trap

Trap diagnosis: agent learned to stall (avoid opening expeditions, draw
from discard pile to extend deck) until max_steps timeout, then squeak by
on opponents' negative scores. All eval wins were from timeouts; agent
never won a naturally-terminating game. Self-play reinforced this because
timeout games still got a positive value target.

Fixes (no algorithm change, all MCTS hyperparameters or signal shaping):

- Dirichlet noise at root prior (AlphaZero standard, was missing):
  mcts.pyx `_expand_with_prior` takes `is_root` flag; root expansion
  mixes prior with Dirichlet(α). Callers in interleaved_self_play and
  the internal evaluate_and_backup pass `not item.path`.
- Default config strengthens exploration on the 50-sim batched search:
  c_puct 1.5 -> 3.0, virtual_loss_value 1.0 -> 5.0, plus new
  root_dirichlet_alpha=0.3 / root_dirichlet_epsilon=0.25.
- Self-play timeout signal zeroed: `_finalize_context` sets v_target=0
  if context.state is not terminal. Stops the network from learning
  "stall = positive value".

New standalone evaluator:
- `lost-cities-ismcts eval` subcommand (eval_checkpoint.py): loads a
  checkpoint, runs N games per opponent across a parallel pool, reports
  win/score with 95% CIs plus per-game logging via --verbose. Defaults
  cover heuristic-balanced/aggressive/cautious (rollout policy isn't in
  the training-eval opponent list, so this is the natural way to compare
  the trained policy against its rollout target).

Tests (19) still pass; .so rebuilt.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

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

@@ -30,6 +30,8 @@ class MctsConfig(StrictModel):
     virtual_loss_value: float = 1.0
     eval_with_mcts: bool = True
     eval_n_simulations: int = 0
+    root_dirichlet_alpha: float = 0.0
+    root_dirichlet_epsilon: float = 0.0
 
     @field_validator("n_simulations", "max_depth", "parallel_simulations")
     @classmethod