Merge branch 'master' into develop-magic-string

gym-unity/gym_unity/__init__.py

-__version__ = "0.12.0"
+__version__ = "0.12.1"

ml-agents-envs/mlagents/envs/__init__.py

-__version__ = "0.12.0"
+__version__ = "0.12.1"

ml-agents/mlagents/trainers/__init__.py

-__version__ = "0.12.0"
+__version__ = "0.12.1"

ml-agents/mlagents/trainers/exception.py

     pass
 
 
+class TrainerConfigError(Exception):
+    """
+    Any error related to the configuration of trainers in the ML-Agents Toolkit.
+    """
+
+    pass
+
+
 class CurriculumError(TrainerError):
     """
     Any error related to training with a curriculum.

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

         pass
 
 
-PPO_CONFIG = """
-    default:
+PPO_CONFIG = f"""
+    {BRAIN_NAME}:
         trainer: ppo
         batch_size: 16
         beta: 5.0e-3
                 gamma: 0.99
     """
 
-SAC_CONFIG = """
-    default:
+SAC_CONFIG = f"""
+    {BRAIN_NAME}:
         trainer: sac
         batch_size: 8
         buffer_size: 500

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

 from mlagents.trainers.trainer_util import load_config, _load_config
 from mlagents.trainers.trainer_metrics import TrainerMetrics
 from mlagents.trainers.ppo.trainer import PPOTrainer
-from mlagents.envs.exception import UnityEnvironmentException
+from mlagents.trainers.exception import TrainerConfigError
+from mlagents.trainers.brain import BrainParameters
 
 
 @pytest.fixture
             use_curiosity: false
             curiosity_strength: 0.0
             curiosity_enc_size: 1
+            reward_signals:
+                extrinsic:
+                    strength: 1.0
+                    gamma: 0.99
         """
     )
 
     BrainParametersMock.return_value.brain_name = "testbrain"
     external_brains = {"testbrain": BrainParametersMock()}
 
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         trainer_factory = trainer_util.TrainerFactory(
             trainer_config=bad_config,
             summaries_dir=summaries_dir,
             trainers[brain_name] = trainer_factory.generate(brain_parameters.brain_name)
 
 
+def test_handles_no_default_section():
+    """
+    Make sure the trainer setup handles a missing "default" in the config.
+    """
+    brain_name = "testbrain"
+    config = dummy_config()
+    no_default_config = {brain_name: config["default"]}
+    brain_parameters = BrainParameters(
+        brain_name=brain_name,
+        vector_observation_space_size=1,
+        camera_resolutions=[],
+        vector_action_space_size=[2],
+        vector_action_descriptions=[],
+        vector_action_space_type=0,
+    )
+
+    trainer_factory = trainer_util.TrainerFactory(
+        trainer_config=no_default_config,
+        summaries_dir="test_dir",
+        run_id="testrun",
+        model_path="model_dir",
+        keep_checkpoints=1,
+        train_model=True,
+        load_model=False,
+        seed=42,
+    )
+    trainer_factory.generate(brain_parameters)
+
+
+def test_raise_if_no_config_for_brain():
+    """
+    Make sure the trainer setup raises a friendlier exception if both "default" and the brain name
+    are missing from the config.
+    """
+    brain_name = "testbrain"
+    config = dummy_config()
+    bad_config = {"some_other_brain": config["default"]}
+    brain_parameters = BrainParameters(
+        brain_name=brain_name,
+        vector_observation_space_size=1,
+        camera_resolutions=[],
+        vector_action_space_size=[2],
+        vector_action_descriptions=[],
+        vector_action_space_type=0,
+    )
+
+    trainer_factory = trainer_util.TrainerFactory(
+        trainer_config=bad_config,
+        summaries_dir="test_dir",
+        run_id="testrun",
+        model_path="model_dir",
+        keep_checkpoints=1,
+        train_model=True,
+        load_model=False,
+        seed=42,
+    )
+    with pytest.raises(TrainerConfigError):
+        trainer_factory.generate(brain_parameters)
+
+
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         load_config("thisFileDefinitelyDoesNotExist.yaml")
 
 
 - not
   - parse
     """
-    with pytest.raises(UnityEnvironmentException):
+    with pytest.raises(TrainerConfigError):
         fp = io.StringIO(file_contents)
         _load_config(fp)

ml-agents/mlagents/trainers/trainer_util.py

 from typing import Any, Dict, TextIO
 
 from mlagents.trainers.meta_curriculum import MetaCurriculum
-from mlagents.envs.exception import UnityEnvironmentException
+from mlagents.trainers.exception import TrainerConfigError
 from mlagents.trainers.trainer import Trainer, UnityTrainerException
 from mlagents.trainers.ppo.trainer import PPOTrainer
 from mlagents.trainers.sac.trainer import SACTrainer
     :param multi_gpu: Whether to use multi-GPU training
     :return:
     """
-    trainer_parameters = trainer_config["default"].copy()
+    if "default" not in trainer_config and brain_name not in trainer_config:
+        raise TrainerConfigError(
+            f'Trainer config must have either a "default" section, or a section for the brain name ({brain_name}). '
+            "See config/trainer_config.yaml for an example."
+        )
+
+    trainer_parameters = trainer_config.get("default", {}).copy()
     trainer_parameters["summary_path"] = "{basedir}/{name}".format(
         basedir=summaries_dir, name=str(run_id) + "_" + brain_name
     )
         trainer_parameters.update(trainer_config[_brain_key])
 
     trainer: Trainer = None  # type: ignore  # will be set to one of these, or raise
-    if trainer_parameters["trainer"] == "offline_bc":
+    if "trainer" not in trainer_parameters:
+        raise TrainerConfigError(
+            f'The "trainer" key must be set in your trainer config for brain {brain_name} (or the default brain).'
+        )
+    trainer_type = trainer_parameters["trainer"]
+
+    if trainer_type == "offline_bc":
-    elif trainer_parameters["trainer"] == "ppo":
+    elif trainer_type == "ppo":
         trainer = PPOTrainer(
             brain_name,
             meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
             run_id,
             multi_gpu,
         )
-    elif trainer_parameters["trainer"] == "sac":
+    elif trainer_type == "sac":
         trainer = SACTrainer(
             brain_name,
             meta_curriculum.brains_to_curriculums[brain_name].min_lesson_length
             run_id,
         )
     else:
-        raise UnityEnvironmentException(
-            "The trainer config contains "
-            "an unknown trainer type for "
-            "brain {}".format(brain_name)
+        raise TrainerConfigError(
+            f'The trainer config contains an unknown trainer type "{trainer_type}" for brain {brain_name}'
         )
     return trainer
 
         with open(config_path) as data_file:
             return _load_config(data_file)
     except IOError:
-        raise UnityEnvironmentException(
-            f"Config file could not be found at {config_path}."
-        )
+        raise TrainerConfigError(f"Config file could not be found at {config_path}.")
-        raise UnityEnvironmentException(
+        raise TrainerConfigError(
             f"There was an error decoding Config file from {config_path}. "
             f"Make sure your file is save using UTF-8"
         )
     try:
         return yaml.safe_load(fp)
     except yaml.parser.ParserError as e:
-        raise UnityEnvironmentException(
+        raise TrainerConfigError(
             "Error parsing yaml file. Please check for formatting errors. "
             "A tool such as http://www.yamllint.com/ can be helpful with this."
         ) from e

notebooks/getting-started.ipynb

    "metadata": {},
    "source": [
     "### 5. Take random actions in the environment\n",
-    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain. \n",
+    "Once we restart an environment, we can step the environment forward and provide actions to all of the agents within the environment. Here we simply choose random actions based on the `action_space_type` of the default brain.\n",
     "\n",
     "Once this cell is executed, 10 messages will be printed that detail how much reward will be accumulated for the next 10 episodes. The Unity environment will then pause, waiting for further signals telling it what to do next. Thus, not seeing any animation is expected when running this cell."
    ]