Deep CFR 재귀 traversal 구현

tests/games/classic/test_deep_cfr_trainer.py

@@ -1,9 +1,11 @@
 from __future__ import annotations
 
-from coolrl_lost_cities.games.classic.game import LostCitiesConfig
+import numpy as np
+from coolrl_lost_cities.games.classic.game import GameState, LostCitiesConfig
 
 from coolrl_lost_cities.games.classic.deep_cfr.config import DeepCFRConfig
 from coolrl_lost_cities.games.classic.deep_cfr.trainer import DeepCFRTrainer
+from coolrl_lost_cities.games.classic.deep_cfr.traverser import DeepCFRTraverser
 
 
 def test_deep_cfr_trainer_smoke_run() -> None:
@@ -11,8 +13,8 @@ def test_deep_cfr_trainer_smoke_run() -> None:
         DeepCFRConfig(
             iterations=1,
             traversals_per_iteration=1,
-            rollouts_per_action=1,
-            max_rollout_steps=64,
+            max_traversal_depth=3,
+            max_nodes_per_traversal=64,
             advantage_train_steps=1,
             strategy_train_steps=1,
             batch_size=2,
@@ -25,7 +27,50 @@ def test_deep_cfr_trainer_smoke_run() -> None:
     metrics = trainer.train()
 
     assert len(metrics) == 1
-    assert metrics[0].advantage_samples == 2
-    assert metrics[0].strategy_samples == 2
+    assert metrics[0].advantage_samples > 0
+    assert metrics[0].strategy_samples > 0
+    assert metrics[0].traversal_nodes > 0
+    assert metrics[0].traversal_max_depth_reached <= 3
     assert metrics[0].advantage_loss >= 0.0
     assert metrics[0].strategy_loss >= 0.0
+
+
+def test_deep_cfr_recursive_traverser_restores_state_and_collects_samples() -> None:
+    trainer = DeepCFRTrainer(
+        DeepCFRConfig(
+            iterations=1,
+            traversals_per_iteration=1,
+            max_traversal_depth=2,
+            max_nodes_per_traversal=32,
+            batch_size=2,
+            hidden_size=16,
+            seed=29,
+        ),
+        LostCitiesConfig(seed=29),
+    )
+    state = GameState.new_game(LostCitiesConfig(seed=29), seed=29)
+    before = state.to_snapshot()
+    traverser = DeepCFRTraverser(
+        trainer.advantage_networks,
+        trainer.advantage_memory,
+        trainer.strategy_memory,
+        device=trainer.device,
+        action_size=trainer.action_size,
+        max_depth=2,
+        max_nodes=32,
+        rng=np.random.default_rng(29),
+    )
+
+    value, stats = traverser.traverse(state, traverser=0, iteration=1)
+
+    assert isinstance(value, float)
+    assert state.to_snapshot() == before
+    assert stats.nodes > 0
+    assert stats.depth_cutoffs + stats.terminals + stats.node_limit_cutoffs > 0
+    assert stats.strategy_samples > 0
+    assert stats.advantage_samples > 0
+    assert len(trainer.strategy_memory) == stats.strategy_samples
+    assert len(trainer.advantage_memory) == stats.advantage_samples
+    sample = trainer.advantage_memory.all()[0]
+    assert sample.legal_mask.dtype == bool
+    assert sample.target.shape == sample.legal_mask.shape