Align interleaved outcome targets and add open diagnostics

tests/games/classic/test_deep_cfr_trainer.py

@@ -459,6 +459,7 @@ def test_deep_cfr_interleaved_scheduler_matches_recursive_single_traversal() ->
                 "opponent_policy": "network",
                 "max_depth": 3,
                 "max_nodes_per_traversal": 64,
+                "outcome_unsampled_regret": "zero",
             },
         }
     )
@@ -522,6 +523,7 @@ def test_deep_cfr_interleaved_scheduler_matches_recursive_single_traversal() ->
             0,
             1,
             **common,
+            outcome_unsampled_regret=config.traversal.outcome_unsampled_regret,
             opponent_policy=config.traversal.opponent_policy,
             interleave_width=4,
             interleave_max_batch=8,
@@ -536,6 +538,10 @@ def test_deep_cfr_interleaved_scheduler_matches_recursive_single_traversal() ->
         [sample.target.sum() for sample in recursive_advantage],
         atol=1.0e-5,
     )
+    for interleaved_sample, recursive_sample in zip(
+        interleaved_advantage, recursive_advantage, strict=True
+    ):
+        assert np.allclose(interleaved_sample.target, recursive_sample.target, atol=1.0e-5)
     assert np.allclose(
         [sample.target.sum() for sample in interleaved_strategy],
         [sample.target.sum() for sample in recursive_strategy],
@@ -619,6 +625,7 @@ def test_deep_cfr_interleaved_scheduler_matches_average_strategy_opponent() -> N
             0,
             1,
             **common,
+            outcome_unsampled_regret=config.traversal.outcome_unsampled_regret,
             opponent_policy=config.traversal.opponent_policy,
             interleave_width=4,
             interleave_max_batch=8,