Add discard_only opponent_policy + analyze.py merges

- Plumb discard_only through config validator and interleaved_traversal.
  Bypasses PolicyRequest for opponent nodes, uses DiscardOnlyBot via
  Snapshot. Recursive scheduler explicitly rejected (Cython unchanged).
- analyze.py: merge per-opponent eval plots into multi-line plots, add
  twin y-axis support (PlotSpec.secondary_metrics), per-axes translucent
  legends instead of one global legend, add avg_game_length to GameFlow.
- Tests: discard_only smoke run, validator accept/reject, all 57 pass.

tests/games/classic/test_deep_cfr_trainer.py

@@ -138,7 +138,9 @@ 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' or 'average_strategy'"):
+    with pytest.raises(
+        ValueError, match="opponent_policy='network', 'average_strategy', or 'discard_only'"
+    ):
         _deep_cfr_config(
             {"traversal": {"scheduler": "interleaved", "opponent_policy": "self_play_league"}}
         )
@@ -154,6 +156,20 @@ def test_deep_cfr_config_rejects_unsupported_interleaved_options() -> None:
         )
 
 
+def test_deep_cfr_config_accepts_discard_only_with_interleaved() -> None:
+    config = _deep_cfr_config(
+        {"traversal": {"scheduler": "interleaved", "opponent_policy": "discard_only"}}
+    )
+    assert config.traversal.opponent_policy == "discard_only"
+
+
+def test_deep_cfr_config_rejects_discard_only_with_recursive() -> None:
+    with pytest.raises(ValueError, match="discard_only.*scheduler='interleaved'"):
+        _deep_cfr_config(
+            {"traversal": {"scheduler": "recursive", "opponent_policy": "discard_only"}}
+        )
+
+
 def test_deep_cfr_train_cli_checkpoint_save_overrides() -> None:
     args = type(
         "Args",
@@ -452,6 +468,42 @@ def test_deep_cfr_trainer_interleaved_scheduler_smoke_run(tmp_path) -> None:
     assert runtime["interleaved/avg_batch_size"] >= 1.0
 
 
+def test_deep_cfr_trainer_discard_only_opponent_smoke_run(tmp_path) -> None:
+    trainer = DeepCFRTrainer(
+        _deep_cfr_config(
+            {
+                "run": {"max_iterations": 1, "seed": 25},
+                "network": {"hidden_size": 16},
+                "traversal": {
+                    "scheduler": "interleaved",
+                    "opponent_policy": "discard_only",
+                    "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=25),
+        run_dir=tmp_path / "discard_only",
+    )
+
+    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(
         {