Add heuristic_balanced opponent_policy to interleaved scheduler

Mirrors the discard_only plumbing pattern. Uses HeuristicBot() (default
balanced params) and converts the bot's phase-local action to unified via
state.to_unified_action. Recursive (Cython) path was already supported
and is unchanged.

Tests: smoke run + accept/reject validators. All 59 tests pass.

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

@@ -197,10 +197,16 @@ class TraversalConfig(StrictModel):
         if self.scheduler == "interleaved":
             if self.sampling_mode != "outcome":
                 raise ValueError("scheduler='interleaved' currently supports only outcome sampling")
-            if self.opponent_policy not in {"network", "average_strategy", "discard_only"}:
+            if self.opponent_policy not in {
+                "network",
+                "average_strategy",
+                "discard_only",
+                "heuristic_balanced",
+            }:
                 raise ValueError(
                     "scheduler='interleaved' currently supports only "
-                    "opponent_policy='network', 'average_strategy', or 'discard_only'"
+                    "opponent_policy='network', 'average_strategy', 'discard_only', "
+                    "or 'heuristic_balanced'"
                 )
             if self.cutoff_rollouts != 0 or self.cutoff_value_mode != "score_diff":
                 raise ValueError(

src/coolrl_lost_cities/games/classic/deep_cfr/interleaved_traversal.py

@@ -9,6 +9,7 @@ import numpy as np
 import torch
 
 from coolrl_lost_cities.games.classic.bots.discard_only import DiscardOnlyBot
+from coolrl_lost_cities.games.classic.bots.heuristic_py import HeuristicBot
 from coolrl_lost_cities.games.classic.deep_cfr.encoding import encode_info_state
 from coolrl_lost_cities.games.classic.deep_cfr.memory import TrainingSample
 from coolrl_lost_cities.games.classic.deep_cfr.traversal_stats import TraversalStats
@@ -381,6 +382,9 @@ class InterleavedContext:
         self._discard_only_bot: DiscardOnlyBot | None = (
             DiscardOnlyBot() if cfg.opponent_policy == "discard_only" else None
         )
+        self._heuristic_bot: HeuristicBot | None = (
+            HeuristicBot() if cfg.opponent_policy == "heuristic_balanced" else None
+        )
 
     def advance_until_policy(self, context_index: int) -> None:
         while not self.done and self.pending is None and self.stack:
@@ -482,6 +486,14 @@ class InterleavedContext:
             self.stack.append(FixedActionFrame(swapped_deck_index=swapped_deck_index))
             self.stack.append(EnterFrame(depth + 1))
             return
+        if player != self.traverser and self._heuristic_bot is not None:
+            phase_local_action = int(self._heuristic_bot.act(self.state))
+            action = int(self.state.to_unified_action(phase_local_action))
+            swapped_deck_index = self._sample_deck_draw_chance(action)
+            self.state.push_unified_action(action)
+            self.stack.append(FixedActionFrame(swapped_deck_index=swapped_deck_index))
+            self.stack.append(EnterFrame(depth + 1))
+            return
         info_state = encode_info_state(self.state, player, self.cfg.encoding)
         legal_mask = np.zeros(self.cfg.action_size, dtype=bool)
         legal_mask[legal_actions] = True

tests/games/classic/test_deep_cfr_trainer.py

@@ -139,7 +139,10 @@ def test_deep_cfr_config_accepts_interleaved_scheduler() -> None:
 
 def test_deep_cfr_config_rejects_unsupported_interleaved_options() -> None:
     with pytest.raises(
-        ValueError, match="opponent_policy='network', 'average_strategy', or 'discard_only'"
+        ValueError,
+        match=(
+            "opponent_policy='network', 'average_strategy', 'discard_only', or 'heuristic_balanced'"
+        ),
     ):
         _deep_cfr_config(
             {"traversal": {"scheduler": "interleaved", "opponent_policy": "self_play_league"}}
@@ -163,6 +166,13 @@ def test_deep_cfr_config_accepts_discard_only_with_interleaved() -> None:
     assert config.traversal.opponent_policy == "discard_only"
 
 
+def test_deep_cfr_config_accepts_heuristic_balanced_with_interleaved() -> None:
+    config = _deep_cfr_config(
+        {"traversal": {"scheduler": "interleaved", "opponent_policy": "heuristic_balanced"}}
+    )
+    assert config.traversal.opponent_policy == "heuristic_balanced"
+
+
 def test_deep_cfr_config_rejects_discard_only_with_recursive() -> None:
     with pytest.raises(ValueError, match="discard_only.*scheduler='interleaved'"):
         _deep_cfr_config(
@@ -504,6 +514,42 @@ def test_deep_cfr_trainer_discard_only_opponent_smoke_run(tmp_path) -> None:
     assert metrics[0].traversal_nodes > 0
 
 
+def test_deep_cfr_trainer_heuristic_balanced_opponent_smoke_run(tmp_path) -> None:
+    trainer = DeepCFRTrainer(
+        _deep_cfr_config(
+            {
+                "run": {"max_iterations": 1, "seed": 27},
+                "network": {"hidden_size": 16},
+                "traversal": {
+                    "scheduler": "interleaved",
+                    "opponent_policy": "heuristic_balanced",
+                    "traversals_per_player": 2,
+                    "max_depth": 3,
+                    "max_nodes_per_traversal": 64,
+                    "interleave_width": 4,
+                    "interleave_max_batch": 8,
+                },
+                "optimization": {
+                    "advantage_updates_per_iteration": 1,
+                    "strategy_updates_per_iteration": 1,
+                    "advantage_batch_size": 2,
+                    "strategy_batch_size": 2,
+                },
+                "checkpoint": {"save_every": 0, "save_latest": False},
+                "evaluation": {"eval_every": 0},
+            }
+        ),
+        LostCitiesConfig(seed=27),
+        run_dir=tmp_path / "heuristic_balanced",
+    )
+
+    metrics = trainer.train()
+
+    assert len(metrics) == 1
+    assert metrics[0].advantage_samples > 0
+    assert metrics[0].traversal_nodes > 0
+
+
 def test_deep_cfr_interleaved_scheduler_matches_recursive_single_traversal() -> None:
     config = _deep_cfr_config(
         {