[refactor] Remove nonfunctional `output_path` option from TrainerSettings (#4087)

config/imitation/CrawlerStatic.yaml

         use_actions: false
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/Crawler/Demos/ExpertCrawlerSta.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000

config/imitation/FoodCollector.yaml

         use_actions: false
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/FoodCollector/Demos/ExpertFood.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 2000000
     time_horizon: 64

config/imitation/Hallway.yaml

         use_actions: false
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/Hallway/Demos/ExpertHallway.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 64

config/imitation/PushBlock.yaml

         use_actions: false
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/PushBlock/Demos/ExpertPush.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 15000000
     time_horizon: 64

config/ppo/3DBall.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 1000

config/ppo/3DBallHard.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 1000

config/ppo/3DBall_randomize.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 1000

config/ppo/Basic.yaml

       extrinsic:
         gamma: 0.9
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 3

config/ppo/Bouncer.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 4000000
     time_horizon: 64

config/ppo/CrawlerDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000

config/ppo/CrawlerStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 1000

config/ppo/FoodCollector.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 2000000
     time_horizon: 64

config/ppo/GridWorld.yaml

       extrinsic:
         gamma: 0.9
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 5

config/ppo/Hallway.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 64

config/ppo/PushBlock.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 2000000
     time_horizon: 64

config/ppo/Pyramids.yaml

         strength: 0.02
         encoding_size: 256
         learning_rate: 0.0003
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 128

config/ppo/Reacher.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 1000

config/ppo/SoccerTwos.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 50000000
     time_horizon: 1000

config/ppo/StrikersVsGoalie.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 50000000
     time_horizon: 1000
       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 50000000
     time_horizon: 1000

config/ppo/Tennis.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 50000000
     time_horizon: 1000

config/ppo/VisualHallway.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 64

config/ppo/VisualPushBlock.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3000000
     time_horizon: 64

config/ppo/VisualPyramids.yaml

         strength: 0.01
         encoding_size: 256
         learning_rate: 0.0003
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 128

config/ppo/WalkerDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 25000000
     time_horizon: 1000

config/ppo/WalkerStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 1000

config/ppo/WallJump.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 128
       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 128

config/ppo/WallJump_curriculum.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 128
       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 128

config/ppo/WormDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3500000
     time_horizon: 1000

config/ppo/WormStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3500000
     time_horizon: 1000

config/sac/3DBall.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 1000

config/sac/3DBallHard.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 1000

config/sac/Basic.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 10

config/sac/Bouncer.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 1000000
     time_horizon: 64

config/sac/CrawlerDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 1000

config/sac/CrawlerStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3000000
     time_horizon: 1000

config/sac/FoodCollector.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 2000000
     time_horizon: 64

config/sac/GridWorld.yaml

       extrinsic:
         gamma: 0.9
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 500000
     time_horizon: 5

config/sac/Hallway.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 64

config/sac/PushBlock.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 2000000
     time_horizon: 64

config/sac/Pyramids.yaml

         use_actions: true
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 128

config/sac/Reacher.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 1000

config/sac/Tennis.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 64

config/sac/VisualHallway.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 64

config/sac/VisualPushBlock.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3000000
     time_horizon: 64

config/sac/VisualPyramids.yaml

         use_actions: true
         use_vail: false
         demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
-    output_path: default
     keep_checkpoints: 5
     max_steps: 10000000
     time_horizon: 128

config/sac/WalkerDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 15000000
     time_horizon: 1000

config/sac/WalkerStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 20000000
     time_horizon: 1000

config/sac/WallJump.yaml

       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 15000000
     time_horizon: 128
       extrinsic:
         gamma: 0.99
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 128

config/sac/WormDynamic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 5000000
     time_horizon: 1000

config/sac/WormStatic.yaml

       extrinsic:
         gamma: 0.995
         strength: 1.0
-    output_path: default
     keep_checkpoints: 5
     max_steps: 3000000
     time_horizon: 1000

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

         reward_buff_cap,
         trainer_settings,
         training,
-        run_id,
+        artifact_path,
     ):
         """
         Creates a GhostTrainer.
         :param reward_buff_cap: Max reward history to track in the reward buffer
         :param trainer_settings: The parameters for the trainer.
         :param training: Whether the trainer is set for training.
-        :param run_id: The identifier of the current run
+        :param artifact_path: Path to store artifacts from this trainer.
-            brain_name, trainer_settings, training, run_id, reward_buff_cap
+            brain_name, trainer_settings, training, artifact_path, reward_buff_cap
         )
 
         self.trainer = trainer

ml-agents/mlagents/trainers/learn.py

         )
         trainer_factory = TrainerFactory(
             options.behaviors,
-            checkpoint_settings.run_id,
             write_path,
             not checkpoint_settings.inference,
             checkpoint_settings.resume,

ml-agents/mlagents/trainers/policy/nn_policy.py

         brain: BrainParameters,
         trainer_params: TrainerSettings,
         is_training: bool,
+        model_path: str,
         load: bool,
         tanh_squash: bool = False,
         reparameterize: bool = False,
         :param trainer_params: Defined training parameters.
         :param is_training: Whether the model should be trained.
         :param load: Whether a pre-trained model will be loaded or a new one created.
+        :param model_path: Path where the model should be saved and loaded.
-        super().__init__(seed, brain, trainer_params, load)
+        super().__init__(seed, brain, trainer_params, model_path, load)
         self.grads = None
         self.update_batch: Optional[tf.Operation] = None
         num_layers = self.network_settings.num_layers

ml-agents/mlagents/trainers/policy/tf_policy.py

         seed: int,
         brain: BrainParameters,
         trainer_settings: TrainerSettings,
+        model_path: str,
         load: bool = False,
     ):
         """
         :param trainer_settings: The trainer parameters.
+        :param model_path: Where to load/save the model.
+        :param load: If True, load model from model_path. Otherwise, create new model.
         """
 
         self.m_size = 0
         self.use_continuous_act = brain.vector_action_space_type == "continuous"
         if self.use_continuous_act:
             self.num_branches = self.brain.vector_action_space_size[0]
-        self.model_path = self.trainer_settings.output_path
+        self.model_path = model_path
         self.initialize_path = self.trainer_settings.init_path
         self.keep_checkpoints = self.trainer_settings.keep_checkpoints
         self.graph = tf.Graph()

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

         training: bool,
         load: bool,
         seed: int,
-        run_id: str,
+        artifact_path: str,
     ):
         """
         Responsible for collecting experiences and training PPO model.
         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The identifier of the current run
+        :param artifact_path: The directory within which to store artifacts from this trainer.
-            brain_name, trainer_settings, training, run_id, reward_buff_cap
+            brain_name, trainer_settings, training, artifact_path, reward_buff_cap
         )
         self.hyperparameters: PPOSettings = cast(
             PPOSettings, self.trainer_settings.hyperparameters
             brain_parameters,
             self.trainer_settings,
             self.is_training,
+            self.artifact_path,
             self.load,
             condition_sigma_on_obs=False,  # Faster training for PPO
             create_tf_graph=False,  # We will create the TF graph in the Optimizer

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

         training: bool,
         load: bool,
         seed: int,
-        run_id: str,
+        artifact_path: str,
     ):
         """
         Responsible for collecting experiences and training SAC model.
         :param training: Whether the trainer is set for training.
         :param load: Whether the model should be loaded.
         :param seed: The seed the model will be initialized with
-        :param run_id: The identifier of the current run
+        :param artifact_path: The directory within which to store artifacts from this trainer.
-            brain_name, trainer_settings, training, run_id, reward_buff_cap
+            brain_name, trainer_settings, training, artifact_path, reward_buff_cap
         )
 
         self.load = load
         """
         Save the training buffer's update buffer to a pickle file.
         """
-        filename = os.path.join(
-            self.trainer_settings.output_path, "last_replay_buffer.hdf5"
-        )
+        filename = os.path.join(self.artifact_path, "last_replay_buffer.hdf5")
         logger.info("Saving Experience Replay Buffer to {}".format(filename))
         with open(filename, "wb") as file_object:
             self.update_buffer.save_to_file(file_object)
         Loads the last saved replay buffer from a file.
         """
-        filename = os.path.join(
-            self.trainer_settings.output_path, "last_replay_buffer.hdf5"
-        )
+        filename = os.path.join(self.artifact_path, "last_replay_buffer.hdf5")
         logger.info("Loading Experience Replay Buffer from {}".format(filename))
         with open(filename, "rb+") as file_object:
             self.update_buffer.load_from_file(file_object)
             brain_parameters,
             self.trainer_settings,
             self.is_training,
+            self.artifact_path,
             self.load,
             tanh_squash=True,
             reparameterize=True,

ml-agents/mlagents/trainers/settings.py

         factory=lambda: {RewardSignalType.EXTRINSIC: RewardSignalSettings()}
     )
     init_path: Optional[str] = None
-    output_path: str = "default"
     keep_checkpoints: int = 5
     checkpoint_interval: int = 500000
     max_steps: int = 500000

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

 @pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
 def test_policy_conversion(tmpdir, rnn, visual, discrete):
     tf.reset_default_graph()
-    dummy_config = TrainerSettings(output_path=os.path.join(tmpdir, "test"))
+    dummy_config = TrainerSettings()
-        dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
+        dummy_config,
+        use_rnn=rnn,
+        model_path=os.path.join(tmpdir, "test"),
+        use_discrete=discrete,
+        use_visual=visual,
     )
     policy.save_model(1000)
     settings = SerializationSettings(

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

         NetworkSettings.MemorySettings() if use_rnn else None
     )
     policy = NNPolicy(
-        0, mock_brain, trainer_config, False, False, tanhresample, tanhresample
+        0, mock_brain, trainer_config, False, "test", False, tanhresample, tanhresample
     )
     with policy.graph.as_default():
         bc_module = BCModule(

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

     use_rnn: bool = False,
     use_discrete: bool = True,
     use_visual: bool = False,
+    model_path: str = "",
     load: bool = False,
     seed: int = 0,
 ) -> NNPolicy:
     trainer_settings.network_settings.memory = (
         NetworkSettings.MemorySettings() if use_rnn else None
     )
-    policy = NNPolicy(seed, mock_brain, trainer_settings, False, load)
+    policy = NNPolicy(seed, mock_brain, trainer_settings, False, model_path, load)
     return policy
 
 
-    trainer_params = TrainerSettings(output_path=path1)
-    policy = create_policy_mock(trainer_params)
+    trainer_params = TrainerSettings()
+    policy = create_policy_mock(trainer_params, model_path=path1)
     policy.initialize_or_load()
     policy._set_step(2000)
     policy.save_model(2000)
     # Try load from this path
-    policy2 = create_policy_mock(trainer_params, load=True, seed=1)
+    policy2 = create_policy_mock(trainer_params, model_path=path1, load=True, seed=1)
     policy2.initialize_or_load()
     _compare_two_policies(policy, policy2)
     assert policy2.get_current_step() == 2000
     trainer_params.init_path = path1
-    policy3 = create_policy_mock(trainer_params, load=False, seed=2)
+    policy3 = create_policy_mock(trainer_params, model_path=path1, load=False, seed=2)
     policy3.initialize_or_load()
 
     _compare_two_policies(policy2, policy3)
         # Test write_stats
         with self.assertLogs("mlagents.trainers", level="WARNING") as cm:
             path1 = tempfile.mkdtemp()
-            trainer_params = TrainerSettings(output_path=path1)
-            policy = create_policy_mock(trainer_params)
+            trainer_params = TrainerSettings()
+            policy = create_policy_mock(trainer_params, model_path=path1)
             policy.initialize_or_load()
             policy._check_model_version(
                 "0.0.0"
         brain_params,
         TrainerSettings(network_settings=NetworkSettings(normalize=True)),
         False,
+        "testdir",
         False,
     )
 

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

 
 def test_take_action_returns_empty_with_no_agents():
     test_seed = 3
-    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings())
+    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings(), "output")
     # Doesn't really matter what this is
     dummy_groupspec = BehaviorSpec([(1,)], "continuous", 1)
     no_agent_step = DecisionSteps.empty(dummy_groupspec)
 
 def test_take_action_returns_nones_on_missing_values():
     test_seed = 3
-    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings())
+    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings(), "output")
     policy.evaluate = MagicMock(return_value={})
     policy.save_memories = MagicMock()
     step_with_agents = DecisionSteps(
 
 def test_take_action_returns_action_info_when_available():
     test_seed = 3
-    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings())
+    policy = FakePolicy(test_seed, basic_mock_brain(), TrainerSettings(), "output")
     policy_eval_out = {
         "action": np.array([1.0], dtype=np.float32),
         "memory_out": np.array([[2.5]], dtype=np.float32),

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

         else None
     )
     policy = NNPolicy(
-        0, mock_brain, trainer_settings, False, False, create_tf_graph=False
+        0, mock_brain, trainer_settings, False, "test", False, create_tf_graph=False
     )
     optimizer = PPOOptimizer(policy, trainer_settings)
     return optimizer

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

         else None
     )
     policy = NNPolicy(
-        0, mock_brain, trainer_settings, False, False, create_tf_graph=False
+        0, mock_brain, trainer_settings, False, "test", False, create_tf_graph=False
     )
     if trainer_settings.trainer_type == TrainerType.SAC:
         optimizer = SACOptimizer(policy, trainer_settings)

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

         else None
     )
     policy = NNPolicy(
-        0, mock_brain, trainer_settings, False, False, create_tf_graph=False
+        0, mock_brain, trainer_settings, False, "test", False, create_tf_graph=False
     )
     optimizer = SACOptimizer(policy, trainer_settings)
     return optimizer
     )
     trainer_params = dummy_config
     trainer_params.hyperparameters.save_replay_buffer = True
-    trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0)
+    trainer = SACTrainer(
+        mock_brain.brain_name, 1, trainer_params, True, False, 0, "testdir"
+    )
     policy = trainer.create_policy(mock_brain.brain_name, mock_brain)
     trainer.add_policy(mock_brain.brain_name, policy)
 
 
     # Wipe Trainer and try to load
-    trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0)
+    trainer2 = SACTrainer(
+        mock_brain.brain_name, 1, trainer_params, True, True, 0, "testdir"
+    )
 
     policy = trainer2.create_policy(mock_brain.brain_name, mock_brain)
     trainer2.add_policy(mock_brain.brain_name, policy)

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

             env_manager = SimpleEnvManager(env, EnvironmentParametersChannel())
         trainer_factory = TrainerFactory(
             trainer_config=trainer_config,
-            run_id=run_id,
             output_path=dir,
             train_model=True,
             load_model=False,

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

     brain_params_mock = BrainParametersMock()
     BrainParametersMock.return_value.brain_name = "testbrain"
     external_brains = {"testbrain": BrainParametersMock()}
-    run_id = "testrun"
     output_path = "results_dir"
     train_model = True
     load_model = False
     expected_config = PPO_CONFIG
 
     def mock_constructor(
-        self, brain, reward_buff_cap, trainer_settings, training, load, seed, run_id
+        self,
+        brain,
+        reward_buff_cap,
+        trainer_settings,
+        training,
+        load,
+        seed,
+        artifact_path,
     ):
         assert brain == brain_params_mock.brain_name
         assert trainer_settings == expected_config
         assert seed == seed
-        assert run_id == run_id
+        assert artifact_path == os.path.join(output_path, brain_name)
-            run_id=run_id,
             output_path=output_path,
             train_model=train_model,
             load_model=load_model,
 
     trainer_factory = trainer_util.TrainerFactory(
         trainer_config=no_default_config,
-        run_id="testrun",
         output_path="output_path",
         train_model=True,
         load_model=False,

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

         brain_name: str,
         trainer_settings: TrainerSettings,
         training: bool,
-        run_id: str,
+        artifact_path: str,
-        :dict trainer_settings: The parameters for the trainer (dictionary).
-        :bool training: Whether the trainer is set for training.
-        :str run_id: The identifier of the current run
-        :int reward_buff_cap:
+        :param trainer_settings: The parameters for the trainer (dictionary).
+        :param training: Whether the trainer is set for training.
+        :param artifact_path: The directory within which to store artifacts from this trainer
+        :param reward_buff_cap:
-        self.run_id = run_id
         self.trainer_settings = trainer_settings
         self._threaded = trainer_settings.threaded
         self._stats_reporter = StatsReporter(brain_name)
         self.trajectory_queues: List[AgentManagerQueue[Trajectory]] = []
         self.step: int = 0
+        self.artifact_path = artifact_path
         self.summary_freq = self.trainer_settings.summary_freq
 
     @property

ml-agents/mlagents/trainers/trainer_util.py

     def __init__(
         self,
         trainer_config: Dict[str, TrainerSettings],
-        run_id: str,
         output_path: str,
         train_model: bool,
         load_model: bool,
         multi_gpu: bool = False,
     ):
         self.trainer_config = trainer_config
-        self.run_id = run_id
         self.output_path = output_path
         self.init_path = init_path
         self.train_model = train_model
         return initialize_trainer(
             self.trainer_config[brain_name],
             brain_name,
-            self.run_id,
             self.output_path,
             self.train_model,
             self.load_model,
 def initialize_trainer(
     trainer_settings: TrainerSettings,
     brain_name: str,
-    run_id: str,
     output_path: str,
     train_model: bool,
     load_model: bool,
 
     :param trainer_settings: Original trainer configuration loaded from YAML
     :param brain_name: Name of the brain to be associated with trainer
-    :param run_id: Run ID to associate with this training run
     :param output_path: Path to save the model and summary statistics
     :param keep_checkpoints: How many model checkpoints to keep
     :param train_model: Whether to train the model (vs. run inference)
     :param meta_curriculum: Optional meta_curriculum, used to determine a reward buffer length for PPOTrainer
     :return:
     """
-    trainer_settings.output_path = os.path.join(output_path, brain_name)
+    trainer_artifact_path = os.path.join(output_path, brain_name)
     if init_path is not None:
         trainer_settings.init_path = os.path.join(init_path, brain_name)
 
             train_model,
             load_model,
             seed,
-            run_id,
+            trainer_artifact_path,
         )
     elif trainer_type == TrainerType.SAC:
         trainer = SACTrainer(
             train_model,
             load_model,
             seed,
-            run_id,
+            trainer_artifact_path,
         )
     else:
         raise TrainerConfigError(
             min_lesson_length,
             trainer_settings,
             train_model,
-            run_id,
+            trainer_artifact_path,
         )
     return trainer