passing all tests locally

ml-agents/mlagents/trainers/ghost/trainer.py

             self.current_policy_snapshot = weights
             self.trainer.add_policy(parsed_behavior_id, policy)
             self._save_snapshot(policy)  # Need to save after trainer initializes policy
-            self.learning_team = team_id
+            self.learning_team = self.controller.get_learning_team(self.ghost_step)
             self.wrapped_trainer_team = team_id
         else:
             # for saving/swapping snapshots

ml-agents/mlagents/trainers/stats.py

 class StatsPropertyType(Enum):
     HYPERPARAMETERS = "hyperparameters"
     SELF_PLAY = "selfplay"
-    SELF_PLAY_TEAM = "selfplayteam"
 
 
 class StatsWriter(abc.ABC):

ml-agents/mlagents/trainers/tests/test_ghost.py

 import yaml
 
 from mlagents.trainers.ghost.trainer import GhostTrainer
+from mlagents.trainers.ghost.controller import GhostController
+from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
-from mlagents.trainers.behavior_id_utils import BehaviorIdentifiers
 from mlagents.trainers.tests import mock_brain as mb
 from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
 
     dummy_config["summary_path"] = "./summaries/test_trainer_summary"
     dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
     ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
-    trainer = GhostTrainer(ppo_trainer, brain_name, 0, dummy_config, True, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
-    trainer.add_policy(brain_params_team0.brain_name, policy)
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(
+        brain_params_team0.brain_name
+    )
+    trainer.add_policy(parsed_behavior_id0, policy)
-    trainer.add_policy(brain_params_team1.brain_name, policy)
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(
+        brain_params_team1.brain_name
+    )
+    trainer.add_policy(parsed_behavior_id1, policy)
     trajectory_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
     trainer.subscribe_trajectory_queue(trajectory_queue1)
 
         vector_action_space_type=0,
     )
 
-    brain_name = BehaviorIdentifiers.from_name_behavior_id(
+    parsed_behavior_id0 = BehaviorIdentifiers.from_name_behavior_id(
-    ).brain_name
+    )
+
+    brain_name = parsed_behavior_id0.brain_name
 
     brain_params_team1 = BrainParameters(
         brain_name="test_brain?team=1",
     dummy_config["summary_path"] = "./summaries/test_trainer_summary"
     dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
     ppo_trainer = PPOTrainer(brain_name, 0, dummy_config, True, False, 0, "0")
-    trainer = GhostTrainer(ppo_trainer, brain_name, 0, dummy_config, True, "0")
+    controller = GhostController(100)
+    trainer = GhostTrainer(
+        ppo_trainer, brain_name, controller, 0, dummy_config, True, "0"
+    )
-    trainer.add_policy(brain_params_team0.brain_name, policy)
+    trainer.add_policy(parsed_behavior_id0, policy)
-    trainer.add_policy(brain_params_team1.brain_name, policy)
+    parsed_behavior_id1 = BehaviorIdentifiers.from_name_behavior_id(
+        brain_params_team1.brain_name
+    )
+    trainer.add_policy(parsed_behavior_id1, policy)
     policy_queue1 = AgentManagerQueue(brain_params_team1.brain_name)
     trainer.publish_policy_queue(policy_queue1)
 

ml-agents/mlagents/trainers/tests/test_stats.py

             category = "category1"
             console_writer = ConsoleWriter()
             console_writer.add_property(category, StatsPropertyType.SELF_PLAY, True)
-            console_writer.add_property(category, StatsPropertyType.SELF_PLAY_TEAM, 1)
             statssummary1 = StatsSummary(mean=1.0, std=1.0, num=1)
             console_writer.write_stats(
                 category,
                     "Self-play/ELO": statssummary1,
-                    "Self-play/Mean Opponent ELO": statssummary1,
-                    "Self-play/Std Opponent ELO": statssummary1,
                 },
                 10,
             )
         )
-        self.assertIn(
-            "category1 Team 1: ELO: 1.000. Mean Opponent ELO: 1.000. Std Opponent ELO: 1.000.",
-            cm.output[1],
-        )

ml-agents/mlagents/trainers/tests/test_trainer_util.py

             keep_checkpoints=keep_checkpoints,
             train_model=train_model,
             load_model=load_model,
+            ghost_swap=100,
             seed=seed,
         )
         trainers = {}
             keep_checkpoints=keep_checkpoints,
             train_model=train_model,
             load_model=load_model,
+            ghost_swap=100,
             seed=seed,
         )
         trainers = {}
     keep_checkpoints = 1
     train_model = True
     load_model = False
+    ghost_swap = 100
     seed = 11
     bad_config = dummy_bad_config
     BrainParametersMock.return_value.brain_name = "testbrain"
             keep_checkpoints=keep_checkpoints,
             train_model=train_model,
             load_model=load_model,
+            ghost_swap=ghost_swap,
             seed=seed,
         )
         trainers = {}
             keep_checkpoints=keep_checkpoints,
             train_model=train_model,
             load_model=load_model,
+            ghost_swap=ghost_swap,
             seed=seed,
         )
         trainers = {}
             keep_checkpoints=keep_checkpoints,
             train_model=train_model,
             load_model=load_model,
+            ghost_swap=ghost_swap,
             seed=seed,
         )
         trainers = {}
         keep_checkpoints=1,
         train_model=True,
         load_model=False,
+        ghost_swap=100,
         seed=42,
     )
     trainer_factory.generate(brain_parameters.brain_name)
         keep_checkpoints=1,
         train_model=True,
         load_model=False,
+        ghost_swap=100,
         seed=42,
     )
     with pytest.raises(TrainerConfigError):