Python code reformat via [`black`](https://github.com/ambv/black).

Features:
- Reformat code via black.
- Adding circleci configurations.
- Add contribution guidelines.

Steps to reproduce:
- `pip install black`
- `black <source code directory>`

.circleci/config.yml

-version: 2.1
+version: 2.0
 
 jobs:
   build:
             . venv/bin/activate
             cd ml-agents-envs && pip install -e .
             cd ../ml-agents && pip install -e .
-            pip install pytest-cov==2.6.1 codacy-coverage==1.3.11
+            pip install black pytest-cov==2.6.1 codacy-coverage==1.3.11
             cd ../gym-unity && pip install -e .
 
       - save_cache:
-        
+
       - run:
           name: Run Tests for ml-agents and gym_unity
           command: |
             python-codacy-coverage -r coverage.xml
+            black --check ml-agents
+            black --check ml-agents-envs
+            black --check gym-unity
 
       - store_test_results:
           path: test-reports

CONTRIBUTING.md

 
 ## Style Guide
 
-When performing changes to the codebase, ensure that you follow the style guide
-of the file you're modifying. For Python, we follow
-[PEP 8](https://www.python.org/dev/peps/pep-0008/).
-For C#, we will soon be adding a formal style guide for our repository.
+When performing changes to the codebase, please ensure that all python code is reformatted using the [black](https://github.com/ambv/black) formatter. For C#, we will soon be requirements for style and formatting.

gym-unity/gym_unity/envs/unity_env.py

     """
     Any error related to the gym wrapper of ml-agents.
     """
+
     pass
 
 
     https://github.com/openai/multiagent-particle-envs
     """
 
-    def __init__(self, environment_filename: str, worker_id=0, use_visual=False, uint8_visual=False, multiagent=False, flatten_branched=False):
+    def __init__(
+        self,
+        environment_filename: str,
+        worker_id=0,
+        use_visual=False,
+        uint8_visual=False,
+        multiagent=False,
+        flatten_branched=False,
+    ):
         """
         Environment initialization
         :param environment_filename: The UnityEnvironment path or file to be wrapped in the gym.
         self._n_agents = None
         self._multiagent = multiagent
         self._flattener = None
-        self.game_over = False # Hidden flag used by Atari environments to determine if the game is over
+        self.game_over = (
+            False
+        )  # Hidden flag used by Atari environments to determine if the game is over
-                "if it is wrapped in a gym.")
+                "if it is wrapped in a gym."
+            )
-            raise UnityGymException("`use_visual` was set to True, however there are no"
-                                    " visual observations as part of this environment.")
+            raise UnityGymException(
+                "`use_visual` was set to True, however there are no"
+                " visual observations as part of this environment."
+            )
-            logger.warning("`uint8_visual was set to true, but visual observations are not in use. "
-                           "This setting will not have any effect.")
+            logger.warning(
+                "`uint8_visual was set to true, but visual observations are not in use. "
+                "This setting will not have any effect."
+            )
-            logger.warning("The environment contains more than one visual observation. "
-                           "Please note that only the first will be provided in the observation.")
+            logger.warning(
+                "The environment contains more than one visual observation. "
+                "Please note that only the first will be provided in the observation."
+            )
-                "if it is wrapped in a gym.")
+                "if it is wrapped in a gym."
+            )
 
         # Check for number of agents in scene.
         initial_info = self._env.reset()[self.brain_name]
                     self._flattener = ActionFlattener(brain.vector_action_space_size)
                     self._action_space = self._flattener.action_space
                 else:
-                    self._action_space = spaces.MultiDiscrete(brain.vector_action_space_size)
+                    self._action_space = spaces.MultiDiscrete(
+                        brain.vector_action_space_size
+                    )
-                logger.warning("The environment has a non-discrete action space. It will "
-                                "not be flattened.")
+                logger.warning(
+                    "The environment has a non-discrete action space. It will "
+                    "not be flattened."
+                )
             high = np.array([1] * brain.vector_action_space_size[0])
             self._action_space = spaces.Box(-high, high, dtype=np.float32)
         high = np.array([np.inf] * brain.vector_observation_space_size)
                 depth = 1
             else:
                 depth = 3
-            self._observation_space = spaces.Box(0, 1, dtype=np.float32,
-                                                 shape=(brain.camera_resolutions[0]["height"],
-                                                        brain.camera_resolutions[0]["width"],
-                                                        depth))
+            self._observation_space = spaces.Box(
+                0,
+                1,
+                dtype=np.float32,
+                shape=(
+                    brain.camera_resolutions[0]["height"],
+                    brain.camera_resolutions[0]["width"],
+                    depth,
+                ),
+            )
         else:
             self._observation_space = spaces.Box(-high, high, dtype=np.float32)
 
         # Use random actions for all other agents in environment.
         if self._multiagent:
             if not isinstance(action, list):
-                raise UnityGymException("The environment was expecting `action` to be a list.")
+                raise UnityGymException(
+                    "The environment was expecting `action` to be a list."
+                )
-                    "The environment was expecting a list of {} actions.".format(self._n_agents))
+                    "The environment was expecting a list of {} actions.".format(
+                        self._n_agents
+                    )
+                )
             else:
                 if self._flattener is not None:
                     # Action space is discrete and flattened - we expect a list of scalars
 
     def _single_step(self, info):
         if self.use_visual:
-            self.visual_obs = self._preprocess_single(info.visual_observations[0][0, :, :, :])
+            self.visual_obs = self._preprocess_single(
+                info.visual_observations[0][0, :, :, :]
+            )
-        return default_observation, info.rewards[0], info.local_done[0], {
-            "text_observation": info.text_observations[0],
-            "brain_info": info}
+        return (
+            default_observation,
+            info.rewards[0],
+            info.local_done[0],
+            {"text_observation": info.text_observations[0], "brain_info": info},
+        )
-            return (255.0*single_visual_obs).astype(np.uint8)
+            return (255.0 * single_visual_obs).astype(np.uint8)
         else:
             return single_visual_obs
 
             default_observation = self.visual_obs
         else:
             default_observation = info.vector_observations
-        return list(default_observation), info.rewards, info.local_done, {
-            "text_observation": info.text_observations,
-            "brain_info": info}
-    
+        return (
+            list(default_observation),
+            info.rewards,
+            info.local_done,
+            {"text_observation": info.text_observations, "brain_info": info},
+        )
+
-            return [(255.0*_visual_obs).astype(np.uint8) for _visual_obs in multiple_visual_obs]
+            return [
+                (255.0 * _visual_obs).astype(np.uint8)
+                for _visual_obs in multiple_visual_obs
+            ]
-    def render(self, mode='rgb_array'):
+    def render(self, mode="rgb_array"):
         return self.visual_obs
 
     def close(self):
         if not self._multiagent and n_agents > 1:
             raise UnityGymException(
                 "The environment was launched as a single-agent environment, however"
-                "there is more than one agent in the scene.")
+                "there is more than one agent in the scene."
+            )
-                "there is only one agent in the scene.")
+                "there is only one agent in the scene."
+            )
-            raise UnityGymException("The number of agents in the environment has changed since "
-                                    "initialization. This is not supported.")
+            raise UnityGymException(
+                "The number of agents in the environment has changed since "
+                "initialization. This is not supported."
+            )
-        return {'render.modes': ['rgb_array']}
+        return {"render.modes": ["rgb_array"]}
-        return -float('inf'), float('inf')
+        return -float("inf"), float("inf")
 
     @property
     def spec(self):
     def number_agents(self):
         return self._n_agents
 
-class ActionFlattener():
+
+class ActionFlattener:
-    def __init__(self,branched_action_space):
+
+    def __init__(self, branched_action_space):
         """
         Initialize the flattener.
         :param branched_action_space: A List containing the sizes of each branch of the action
         possible_vals = [range(_num) for _num in branched_action_space]
         all_actions = [list(_action) for _action in itertools.product(*possible_vals)]
         # Dict should be faster than List for large action spaces
-        action_lookup = {_scalar: _action for (_scalar, _action) in enumerate(all_actions)}
+        action_lookup = {
+            _scalar: _action for (_scalar, _action) in enumerate(all_actions)
+        }
         return action_lookup
 
     def lookup_action(self, action):

gym-unity/gym_unity/tests/test_gym.py

 from gym_unity.envs import UnityEnv, UnityGymException
 
 
-@mock.patch('gym_unity.envs.unity_env.UnityEnvironment')
+@mock.patch("gym_unity.envs.unity_env.UnityEnvironment")
-    env = UnityEnv(' ', use_visual=False, multiagent=False)
+    env = UnityEnv(" ", use_visual=False, multiagent=False)
     assert isinstance(env, UnityEnv)
     assert isinstance(env.reset(), np.ndarray)
     actions = env.action_space.sample()
     assert isinstance(info, dict)
 
 
-@mock.patch('gym_unity.envs.unity_env.UnityEnvironment')
+@mock.patch("gym_unity.envs.unity_env.UnityEnvironment")
 def test_multi_agent(mock_env):
     mock_brain = create_mock_brainparams()
     mock_braininfo = create_mock_vector_braininfo(num_agents=2)
-        UnityEnv(' ', multiagent=False)
+        UnityEnv(" ", multiagent=False)
-    env = UnityEnv(' ', use_visual=False, multiagent=True)
+    env = UnityEnv(" ", use_visual=False, multiagent=True)
     assert isinstance(env.reset(), list)
     actions = [env.action_space.sample() for i in range(env.number_agents)]
     obs, rew, done, info = env.step(actions)
     assert isinstance(info, dict)
 
 
-@mock.patch('gym_unity.envs.unity_env.UnityEnvironment')
+@mock.patch("gym_unity.envs.unity_env.UnityEnvironment")
-    mock_brain = create_mock_brainparams(vector_action_space_type='discrete', vector_action_space_size=[2,2,3])
+    mock_brain = create_mock_brainparams(
+        vector_action_space_type="discrete", vector_action_space_size=[2, 2, 3]
+    )
-    env = UnityEnv(' ', use_visual=False, multiagent=False, flatten_branched=True)
+    env = UnityEnv(" ", use_visual=False, multiagent=False, flatten_branched=True)
-    assert env.action_space.n==12
-    assert env._flattener.lookup_action(0)==[0,0,0]
-    assert env._flattener.lookup_action(11)==[1,1,2]
+    assert env.action_space.n == 12
+    assert env._flattener.lookup_action(0) == [0, 0, 0]
+    assert env._flattener.lookup_action(11) == [1, 1, 2]
-    env = UnityEnv(' ', use_visual=False, multiagent=False, flatten_branched=False)
+    env = UnityEnv(" ", use_visual=False, multiagent=False, flatten_branched=False)
+
-def create_mock_brainparams(number_visual_observations=0, num_stacked_vector_observations=1,
-                            vector_action_space_type='continuous', vector_observation_space_size=3,
-                            vector_action_space_size=None):
+def create_mock_brainparams(
+    number_visual_observations=0,
+    num_stacked_vector_observations=1,
+    vector_action_space_type="continuous",
+    vector_observation_space_size=3,
+    vector_action_space_size=None,
+):
     """
     Creates a mock BrainParameters object with parameters.
     """
     mock_brain = mock.Mock()
     mock_brain.return_value.number_visual_observations = number_visual_observations
-    mock_brain.return_value.num_stacked_vector_observations = num_stacked_vector_observations
+    mock_brain.return_value.num_stacked_vector_observations = (
+        num_stacked_vector_observations
+    )
-    mock_brain.return_value.vector_observation_space_size = vector_observation_space_size
+    mock_brain.return_value.vector_observation_space_size = (
+        vector_observation_space_size
+    )
-def create_mock_vector_braininfo(num_agents = 1):
+def create_mock_vector_braininfo(num_agents=1):
     """
     Creates a mock BrainInfo with vector observations. Imitates constant
     vector observations, rewards, dones, and agents.
     mock_braininfo = mock.Mock()
-    mock_braininfo.return_value.vector_observations = np.array([num_agents*[1, 2, 3,]])
-    mock_braininfo.return_value.rewards = num_agents*[1.0]
-    mock_braininfo.return_value.local_done = num_agents*[False]
-    mock_braininfo.return_value.text_observations = num_agents*['']
-    mock_braininfo.return_value.agents = range(0,num_agents)
+    mock_braininfo.return_value.vector_observations = np.array([num_agents * [1, 2, 3]])
+    mock_braininfo.return_value.rewards = num_agents * [1.0]
+    mock_braininfo.return_value.local_done = num_agents * [False]
+    mock_braininfo.return_value.text_observations = num_agents * [""]
+    mock_braininfo.return_value.agents = range(0, num_agents)
     return mock_braininfo()
 
 
     :Mock mock_brain: A mock Brain object that specifies the params of this environment.
     :Mock mock_braininfo: A mock BrainInfo object that will be returned at each step and reset.
     """
-    mock_env.return_value.academy_name = 'MockAcademy'
-    mock_env.return_value.brains = {'MockBrain':mock_brain}
-    mock_env.return_value.external_brain_names = ['MockBrain']
-    mock_env.return_value.reset.return_value = {'MockBrain':mock_braininfo}
-    mock_env.return_value.step.return_value = {'MockBrain':mock_braininfo}
+    mock_env.return_value.academy_name = "MockAcademy"
+    mock_env.return_value.brains = {"MockBrain": mock_brain}
+    mock_env.return_value.external_brain_names = ["MockBrain"]
+    mock_env.return_value.reset.return_value = {"MockBrain": mock_braininfo}
+    mock_env.return_value.step.return_value = {"MockBrain": mock_braininfo}

gym-unity/setup.py

 
 from setuptools import setup, find_packages
 
-setup(name='gym_unity',
-      version='0.4.1',
-      description='Unity Machine Learning Agents Gym Interface',
-      license='Apache License 2.0',
-      author='Unity Technologies',
-      author_email='ML-Agents@unity3d.com',
-      url='https://github.com/Unity-Technologies/ml-agents',
-      packages=find_packages(),
-      install_requires=['gym', 'mlagents_envs==0.8.1']
-      )
+setup(
+    name="gym_unity",
+    version="0.4.1",
+    description="Unity Machine Learning Agents Gym Interface",
+    license="Apache License 2.0",
+    author="Unity Technologies",
+    author_email="ML-Agents@unity3d.com",
+    url="https://github.com/Unity-Technologies/ml-agents",
+    packages=find_packages(),
+    install_requires=["gym", "mlagents_envs==0.8.1"],
+)

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

 
 class BaseUnityEnvironment(ABC):
     @abstractmethod
-    def step(self, vector_action=None, memory=None, text_action=None, value=None) -> AllBrainInfo:
+    def step(
+        self, vector_action=None, memory=None, text_action=None, value=None
+    ) -> AllBrainInfo:
         pass
 
     @abstractmethod

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

 
 
 class BrainInfo:
-    def __init__(self, visual_observation, vector_observation, text_observations, memory=None,
-                 reward=None, agents=None, local_done=None,
-                 vector_action=None, text_action=None, max_reached=None, action_mask=None,
-                 custom_observations=None):
+    def __init__(
+        self,
+        visual_observation,
+        vector_observation,
+        text_observations,
+        memory=None,
+        reward=None,
+        agents=None,
+        local_done=None,
+        vector_action=None,
+        text_action=None,
+        max_reached=None,
+        action_mask=None,
+        custom_observations=None,
+    ):
         """
         Describes experience at current step of all agents linked to a brain.
         """
     def merge(self, other):
         for i in range(len(self.visual_observations)):
             self.visual_observations[i].extend(other.visual_observations[i])
-        self.vector_observations = np.append(self.vector_observations, other.vector_observations, axis=0)
+        self.vector_observations = np.append(
+            self.vector_observations, other.vector_observations, axis=0
+        )
-        self.memories = self.merge_memories(self.memories, other.memories, self.agents, other.agents)
+        self.memories = self.merge_memories(
+            self.memories, other.memories, self.agents, other.agents
+        )
         self.rewards = safe_concat_lists(self.rewards, other.rewards)
         self.local_done = safe_concat_lists(self.local_done, other.local_done)
         self.max_reached = safe_concat_lists(self.max_reached, other.max_reached)
         self.previous_text_actions = safe_concat_lists(
             self.previous_text_actions, other.previous_text_actions
         )
-        self.action_masks = safe_concat_np_ndarray(self.action_masks, other.action_masks)
-        self.custom_observations = safe_concat_lists(self.custom_observations, other.custom_observations)
+        self.action_masks = safe_concat_np_ndarray(
+            self.action_masks, other.action_masks
+        )
+        self.custom_observations = safe_concat_lists(
+            self.custom_observations, other.custom_observations
+        )
 
     @staticmethod
     def merge_memories(m1, m2, agents1, agents2):
             m2 = np.zeros((len(agents2), m1.shape[1]))
         elif m2.shape[1] > m1.shape[1]:
             new_m1 = np.zeros((m1.shape[0], m2.shape[1]))
-            new_m1[0:m1.shape[0], 0:m1.shape[1]] = m1
+            new_m1[0 : m1.shape[0], 0 : m1.shape[1]] = m1
-            new_m2[0:m2.shape[0], 0:m2.shape[1]] = m2
+            new_m2[0 : m2.shape[0], 0 : m2.shape[1]] = m2
             return np.append(m1, new_m2, axis=0)
         return np.append(m1, m2, axis=0)
 
         """
         vis_obs = []
         for i in range(brain_params.number_visual_observations):
-            obs = [BrainInfo.process_pixels(x.visual_observations[i],
-                                            brain_params.camera_resolutions[i]['blackAndWhite'])
-                   for x in agent_info_list]
+            obs = [
+                BrainInfo.process_pixels(
+                    x.visual_observations[i],
+                    brain_params.camera_resolutions[i]["blackAndWhite"],
+                )
+                for x in agent_info_list
+            ]
             vis_obs += [obs]
         if len(agent_info_list) == 0:
             memory_size = 0
             memory = np.zeros((0, 0))
         else:
-            [x.memories.extend([0] * (memory_size - len(x.memories))) for x in agent_info_list]
+            [
+                x.memories.extend([0] * (memory_size - len(x.memories)))
+                for x in agent_info_list
+            ]
             memory = np.array([list(x.memories) for x in agent_info_list])
         total_num_actions = sum(brain_params.vector_action_space_size)
         mask_actions = np.ones((len(agent_info_list), total_num_actions))
                     mask_actions[agent_index, :] = [
-                        0 if agent_info.action_mask[k] else 1 for k in range(total_num_actions)]
+                        0 if agent_info.action_mask[k] else 1
+                        for k in range(total_num_actions)
+                    ]
-            logger.warning("An agent had a NaN reward for brain " + brain_params.brain_name)
+            logger.warning(
+                "An agent had a NaN reward for brain " + brain_params.brain_name
+            )
-            logger.warning("An agent had a NaN observation for brain " + brain_params.brain_name)
+            logger.warning(
+                "An agent had a NaN observation for brain " + brain_params.brain_name
+            )
-                (0, brain_params.vector_observation_space_size * brain_params.num_stacked_vector_observations)
+                (
+                    0,
+                    brain_params.vector_observation_space_size
+                    * brain_params.num_stacked_vector_observations,
+                )
             )
         else:
             vector_obs = np.nan_to_num(
             text_action=[list(x.stored_text_actions) for x in agent_info_list],
             max_reached=[x.max_step_reached for x in agent_info_list],
             custom_observations=[x.custom_observation for x in agent_info_list],
-            action_mask=mask_actions
+            action_mask=mask_actions,
         )
         return brain_info
 
 
 
 class BrainParameters:
-    def __init__(self,
-                 brain_name: str,
-                 vector_observation_space_size: int,
-                 num_stacked_vector_observations: int,
-                 camera_resolutions: List[Dict],
-                 vector_action_space_size: List[int],
-                 vector_action_descriptions: List[str],
-                 vector_action_space_type: int):
+    def __init__(
+        self,
+        brain_name: str,
+        vector_observation_space_size: int,
+        num_stacked_vector_observations: int,
+        camera_resolutions: List[Dict],
+        vector_action_space_size: List[int],
+        vector_action_descriptions: List[str],
+        vector_action_space_type: int,
+    ):
         """
         Contains all brain-specific parameters.
         """
         self.camera_resolutions = camera_resolutions
         self.vector_action_space_size = vector_action_space_size
         self.vector_action_descriptions = vector_action_descriptions
-        self.vector_action_space_type = ["discrete", "continuous"][vector_action_space_type]
+        self.vector_action_space_type = ["discrete", "continuous"][
+            vector_action_space_type
+        ]
-        return '''Unity brain name: {}
+        return """Unity brain name: {}
-        Vector Action descriptions: {}'''.format(self.brain_name,
-                                                 str(self.number_visual_observations),
-                                                 str(self.vector_observation_space_size),
-                                                 str(self.num_stacked_vector_observations),
-                                                 self.vector_action_space_type,
-                                                 str(self.vector_action_space_size),
-                                                 ', '.join(self.vector_action_descriptions))
+        Vector Action descriptions: {}""".format(
+            self.brain_name,
+            str(self.number_visual_observations),
+            str(self.vector_observation_space_size),
+            str(self.num_stacked_vector_observations),
+            self.vector_action_space_type,
+            str(self.vector_action_space_size),
+            ", ".join(self.vector_action_descriptions),
+        )
 
     @staticmethod
     def from_proto(brain_param_proto):
         :return: BrainParameter object.
         """
-        resolution = [{
-            "height": x.height,
-            "width": x.width,
-            "blackAndWhite": x.gray_scale
-        } for x in brain_param_proto.camera_resolutions]
-        brain_params = BrainParameters(brain_param_proto.brain_name,
-                                       brain_param_proto.vector_observation_size,
-                                       brain_param_proto.num_stacked_vector_observations,
-                                       resolution,
-                                       list(brain_param_proto.vector_action_size),
-                                       list(brain_param_proto.vector_action_descriptions),
-                                       brain_param_proto.vector_action_space_type)
-        return brain_params
+        resolution = [
+            {"height": x.height, "width": x.width, "blackAndWhite": x.gray_scale}
+            for x in brain_param_proto.camera_resolutions
+        ]
+        brain_params = BrainParameters(
+            brain_param_proto.brain_name,
+            brain_param_proto.vector_observation_size,
+            brain_param_proto.num_stacked_vector_observations,
+            resolution,
+            list(brain_param_proto.vector_action_size),
+            list(brain_param_proto.vector_action_descriptions),
+            brain_param_proto.vector_action_space_type,
+        )
+        return brain_params

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

         """
         Sends a shutdown signal to the unity environment, and closes the connection.
         """
-

ml-agents-envs/mlagents/envs/communicator_objects/agent_action_proto_pb2.py

 # source: mlagents/envs/communicator_objects/agent_action_proto.proto
 
 import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
+
-from mlagents.envs.communicator_objects import custom_action_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2
+from mlagents.envs.communicator_objects import (
+    custom_action_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2,
+)
-  name='mlagents/envs/communicator_objects/agent_action_proto.proto',
-  package='communicator_objects',
-  syntax='proto3',
-  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
-  serialized_pb=_b('\n;mlagents/envs/communicator_objects/agent_action_proto.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents/envs/communicator_objects/custom_action.proto\"\x9c\x01\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\x14\n\x0ctext_actions\x18\x02 \x01(\t\x12\x10\n\x08memories\x18\x03 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02\x12\x39\n\rcustom_action\x18\x05 \x01(\x0b\x32\".communicator_objects.CustomActionB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  ,
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2.DESCRIPTOR,])
-
+    name="mlagents/envs/communicator_objects/agent_action_proto.proto",
+    package="communicator_objects",
+    syntax="proto3",
+    serialized_options=_b("\252\002\034MLAgents.CommunicatorObjects"),
+    serialized_pb=_b(
+        '\n;mlagents/envs/communicator_objects/agent_action_proto.proto\x12\x14\x63ommunicator_objects\x1a\x36mlagents/envs/communicator_objects/custom_action.proto"\x9c\x01\n\x10\x41gentActionProto\x12\x16\n\x0evector_actions\x18\x01 \x03(\x02\x12\x14\n\x0ctext_actions\x18\x02 \x01(\t\x12\x10\n\x08memories\x18\x03 \x03(\x02\x12\r\n\x05value\x18\x04 \x01(\x02\x12\x39\n\rcustom_action\x18\x05 \x01(\x0b\x32".communicator_objects.CustomActionB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3'
+    ),
+    dependencies=[
+        mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2.DESCRIPTOR
+    ],
+)
-
-  name='AgentActionProto',
-  full_name='communicator_objects.AgentActionProto',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='vector_actions', full_name='communicator_objects.AgentActionProto.vector_actions', index=0,
-      number=1, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='text_actions', full_name='communicator_objects.AgentActionProto.text_actions', index=1,
-      number=2, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='memories', full_name='communicator_objects.AgentActionProto.memories', index=2,
-      number=3, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='value', full_name='communicator_objects.AgentActionProto.value', index=3,
-      number=4, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='custom_action', full_name='communicator_objects.AgentActionProto.custom_action', index=4,
-      number=5, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=142,
-  serialized_end=298,
+    name="AgentActionProto",
+    full_name="communicator_objects.AgentActionProto",
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name="vector_actions",
+            full_name="communicator_objects.AgentActionProto.vector_actions",
+            index=0,
+            number=1,
+            type=2,
+            cpp_type=6,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="text_actions",
+            full_name="communicator_objects.AgentActionProto.text_actions",
+            index=1,
+            number=2,
+            type=9,
+            cpp_type=9,
+            label=1,
+            has_default_value=False,
+            default_value=_b("").decode("utf-8"),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="memories",
+            full_name="communicator_objects.AgentActionProto.memories",
+            index=2,
+            number=3,
+            type=2,
+            cpp_type=6,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="value",
+            full_name="communicator_objects.AgentActionProto.value",
+            index=3,
+            number=4,
+            type=2,
+            cpp_type=6,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="custom_action",
+            full_name="communicator_objects.AgentActionProto.custom_action",
+            index=4,
+            number=5,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    serialized_options=None,
+    is_extendable=False,
+    syntax="proto3",
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=142,
+    serialized_end=298,
-_AGENTACTIONPROTO.fields_by_name['custom_action'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2._CUSTOMACTION
-DESCRIPTOR.message_types_by_name['AgentActionProto'] = _AGENTACTIONPROTO
+_AGENTACTIONPROTO.fields_by_name[
+    "custom_action"
+].message_type = (
+    mlagents_dot_envs_dot_communicator__objects_dot_custom__action__pb2._CUSTOMACTION
+)
+DESCRIPTOR.message_types_by_name["AgentActionProto"] = _AGENTACTIONPROTO
-AgentActionProto = _reflection.GeneratedProtocolMessageType('AgentActionProto', (_message.Message,), dict(
-  DESCRIPTOR = _AGENTACTIONPROTO,
-  __module__ = 'mlagents.envs.communicator_objects.agent_action_proto_pb2'
-  # @@protoc_insertion_point(class_scope:communicator_objects.AgentActionProto)
-  ))
+AgentActionProto = _reflection.GeneratedProtocolMessageType(
+    "AgentActionProto",
+    (_message.Message,),
+    dict(
+        DESCRIPTOR=_AGENTACTIONPROTO,
+        __module__="mlagents.envs.communicator_objects.agent_action_proto_pb2"
+        # @@protoc_insertion_point(class_scope:communicator_objects.AgentActionProto)
+    ),
+)
 _sym_db.RegisterMessage(AgentActionProto)
 
 

ml-agents-envs/mlagents/envs/communicator_objects/agent_info_proto_pb2.py

 # source: mlagents/envs/communicator_objects/agent_info_proto.proto
 
 import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
+
-from mlagents.envs.communicator_objects import custom_observation_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2
+from mlagents.envs.communicator_objects import (
+    custom_observation_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2,
+)
-  name='mlagents/envs/communicator_objects/agent_info_proto.proto',
-  package='communicator_objects',
-  syntax='proto3',
-  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
-  serialized_pb=_b('\n9mlagents/envs/communicator_objects/agent_info_proto.proto\x12\x14\x63ommunicator_objects\x1a;mlagents/envs/communicator_objects/custom_observation.proto\"\xd7\x02\n\x0e\x41gentInfoProto\x12\"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x1b\n\x13visual_observations\x18\x02 \x03(\x0c\x12\x18\n\x10text_observation\x18\x03 \x01(\t\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x1b\n\x13stored_text_actions\x18\x05 \x01(\t\x12\x10\n\x08memories\x18\x06 \x03(\x02\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12\x43\n\x12\x63ustom_observation\x18\x0c \x01(\x0b\x32\'.communicator_objects.CustomObservationB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  ,
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2.DESCRIPTOR,])
-
-
+    name="mlagents/envs/communicator_objects/agent_info_proto.proto",
+    package="communicator_objects",
+    syntax="proto3",
+    serialized_options=_b("\252\002\034MLAgents.CommunicatorObjects"),
+    serialized_pb=_b(
+        '\n9mlagents/envs/communicator_objects/agent_info_proto.proto\x12\x14\x63ommunicator_objects\x1a;mlagents/envs/communicator_objects/custom_observation.proto"\xd7\x02\n\x0e\x41gentInfoProto\x12"\n\x1astacked_vector_observation\x18\x01 \x03(\x02\x12\x1b\n\x13visual_observations\x18\x02 \x03(\x0c\x12\x18\n\x10text_observation\x18\x03 \x01(\t\x12\x1d\n\x15stored_vector_actions\x18\x04 \x03(\x02\x12\x1b\n\x13stored_text_actions\x18\x05 \x01(\t\x12\x10\n\x08memories\x18\x06 \x03(\x02\x12\x0e\n\x06reward\x18\x07 \x01(\x02\x12\x0c\n\x04\x64one\x18\x08 \x01(\x08\x12\x18\n\x10max_step_reached\x18\t \x01(\x08\x12\n\n\x02id\x18\n \x01(\x05\x12\x13\n\x0b\x61\x63tion_mask\x18\x0b \x03(\x08\x12\x43\n\x12\x63ustom_observation\x18\x0c \x01(\x0b\x32\'.communicator_objects.CustomObservationB\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3'
+    ),
+    dependencies=[
+        mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2.DESCRIPTOR
+    ],
+)
-  name='AgentInfoProto',
-  full_name='communicator_objects.AgentInfoProto',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='stacked_vector_observation', full_name='communicator_objects.AgentInfoProto.stacked_vector_observation', index=0,
-      number=1, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='visual_observations', full_name='communicator_objects.AgentInfoProto.visual_observations', index=1,
-      number=2, type=12, cpp_type=9, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='text_observation', full_name='communicator_objects.AgentInfoProto.text_observation', index=2,
-      number=3, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='stored_vector_actions', full_name='communicator_objects.AgentInfoProto.stored_vector_actions', index=3,
-      number=4, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='stored_text_actions', full_name='communicator_objects.AgentInfoProto.stored_text_actions', index=4,
-      number=5, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='memories', full_name='communicator_objects.AgentInfoProto.memories', index=5,
-      number=6, type=2, cpp_type=6, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='reward', full_name='communicator_objects.AgentInfoProto.reward', index=6,
-      number=7, type=2, cpp_type=6, label=1,
-      has_default_value=False, default_value=float(0),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='done', full_name='communicator_objects.AgentInfoProto.done', index=7,
-      number=8, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='max_step_reached', full_name='communicator_objects.AgentInfoProto.max_step_reached', index=8,
-      number=9, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='id', full_name='communicator_objects.AgentInfoProto.id', index=9,
-      number=10, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='action_mask', full_name='communicator_objects.AgentInfoProto.action_mask', index=10,
-      number=11, type=8, cpp_type=7, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='custom_observation', full_name='communicator_objects.AgentInfoProto.custom_observation', index=11,
-      number=12, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=145,
-  serialized_end=488,
+    name="AgentInfoProto",
+    full_name="communicator_objects.AgentInfoProto",
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name="stacked_vector_observation",
+            full_name="communicator_objects.AgentInfoProto.stacked_vector_observation",
+            index=0,
+            number=1,
+            type=2,
+            cpp_type=6,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="visual_observations",
+            full_name="communicator_objects.AgentInfoProto.visual_observations",
+            index=1,
+            number=2,
+            type=12,
+            cpp_type=9,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="text_observation",
+            full_name="communicator_objects.AgentInfoProto.text_observation",
+            index=2,
+            number=3,
+            type=9,
+            cpp_type=9,
+            label=1,
+            has_default_value=False,
+            default_value=_b("").decode("utf-8"),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="stored_vector_actions",
+            full_name="communicator_objects.AgentInfoProto.stored_vector_actions",
+            index=3,
+            number=4,
+            type=2,
+            cpp_type=6,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="stored_text_actions",
+            full_name="communicator_objects.AgentInfoProto.stored_text_actions",
+            index=4,
+            number=5,
+            type=9,
+            cpp_type=9,
+            label=1,
+            has_default_value=False,
+            default_value=_b("").decode("utf-8"),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="memories",
+            full_name="communicator_objects.AgentInfoProto.memories",
+            index=5,
+            number=6,
+            type=2,
+            cpp_type=6,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="reward",
+            full_name="communicator_objects.AgentInfoProto.reward",
+            index=6,
+            number=7,
+            type=2,
+            cpp_type=6,
+            label=1,
+            has_default_value=False,
+            default_value=float(0),
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="done",
+            full_name="communicator_objects.AgentInfoProto.done",
+            index=7,
+            number=8,
+            type=8,
+            cpp_type=7,
+            label=1,
+            has_default_value=False,
+            default_value=False,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="max_step_reached",
+            full_name="communicator_objects.AgentInfoProto.max_step_reached",
+            index=8,
+            number=9,
+            type=8,
+            cpp_type=7,
+            label=1,
+            has_default_value=False,
+            default_value=False,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="id",
+            full_name="communicator_objects.AgentInfoProto.id",
+            index=9,
+            number=10,
+            type=5,
+            cpp_type=1,
+            label=1,
+            has_default_value=False,
+            default_value=0,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="action_mask",
+            full_name="communicator_objects.AgentInfoProto.action_mask",
+            index=10,
+            number=11,
+            type=8,
+            cpp_type=7,
+            label=3,
+            has_default_value=False,
+            default_value=[],
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="custom_observation",
+            full_name="communicator_objects.AgentInfoProto.custom_observation",
+            index=11,
+            number=12,
+            type=11,
+            cpp_type=10,
+            label=1,
+            has_default_value=False,
+            default_value=None,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+    ],
+    extensions=[],
+    nested_types=[],
+    enum_types=[],
+    serialized_options=None,
+    is_extendable=False,
+    syntax="proto3",
+    extension_ranges=[],
+    oneofs=[],
+    serialized_start=145,
+    serialized_end=488,
-_AGENTINFOPROTO.fields_by_name['custom_observation'].message_type = mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2._CUSTOMOBSERVATION
-DESCRIPTOR.message_types_by_name['AgentInfoProto'] = _AGENTINFOPROTO
+_AGENTINFOPROTO.fields_by_name[
+    "custom_observation"
+].message_type = (
+    mlagents_dot_envs_dot_communicator__objects_dot_custom__observation__pb2._CUSTOMOBSERVATION
+)
+DESCRIPTOR.message_types_by_name["AgentInfoProto"] = _AGENTINFOPROTO
-AgentInfoProto = _reflection.GeneratedProtocolMessageType('AgentInfoProto', (_message.Message,), dict(
-  DESCRIPTOR = _AGENTINFOPROTO,
-  __module__ = 'mlagents.envs.communicator_objects.agent_info_proto_pb2'
-  # @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoProto)
-  ))
+AgentInfoProto = _reflection.GeneratedProtocolMessageType(
+    "AgentInfoProto",
+    (_message.Message,),
+    dict(
+        DESCRIPTOR=_AGENTINFOPROTO,
+        __module__="mlagents.envs.communicator_objects.agent_info_proto_pb2"
+        # @@protoc_insertion_point(class_scope:communicator_objects.AgentInfoProto)
+    ),
+)
 _sym_db.RegisterMessage(AgentInfoProto)
 
 

ml-agents-envs/mlagents/envs/communicator_objects/brain_parameters_proto_pb2.py

 # source: mlagents/envs/communicator_objects/brain_parameters_proto.proto
 
 import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
+
+_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
+
-from mlagents.envs.communicator_objects import resolution_proto_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_resolution__proto__pb2
-from mlagents.envs.communicator_objects import space_type_proto_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_space__type__proto__pb2
+from mlagents.envs.communicator_objects import (
+    resolution_proto_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_resolution__proto__pb2,
+)
+from mlagents.envs.communicator_objects import (
+    space_type_proto_pb2 as mlagents_dot_envs_dot_communicator__objects_dot_space__type__proto__pb2,
+)
-  name='mlagents/envs/communicator_objects/brain_parameters_proto.proto',
-  package='communicator_objects',
-  syntax='proto3',
-  serialized_options=_b('\252\002\034MLAgents.CommunicatorObjects'),
-  serialized_pb=_b('\n?mlagents/envs/communicator_objects/brain_parameters_proto.proto\x12\x14\x63ommunicator_objects\x1a\x39mlagents/envs/communicator_objects/resolution_proto.proto\x1a\x39mlagents/envs/communicator_objects/space_type_proto.proto\"\xd4\x02\n\x14\x42rainParametersProto\x12\x1f\n\x17vector_observation_size\x18\x01 \x01(\x05\x12\'\n\x1fnum_stacked_vector_observations\x18\x02 \x01(\x05\x12\x1a\n\x12vector_action_size\x18\x03 \x03(\x05\x12\x41\n\x12\x63\x61mera_resolutions\x18\x04 \x03(\x0b\x32%.communicator_objects.ResolutionProto\x12\"\n\x1avector_action_descriptions\x18\x05 \x03(\t\x12\x46\n\x18vector_action_space_type\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x13\n\x0bis_training\x18\x08 \x01(\x08\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3')
-  ,
-  dependencies=[mlagents_dot_envs_dot_communicator__objects_dot_resolution__proto__pb2.DESCRIPTOR,mlagents_dot_envs_dot_communicator__objects_dot_space__type__proto__pb2.DESCRIPTOR,])
-
-
+    name="mlagents/envs/communicator_objects/brain_parameters_proto.proto",
+    package="communicator_objects",
+    syntax="proto3",
+    serialized_options=_b("\252\002\034MLAgents.CommunicatorObjects"),
+    serialized_pb=_b(
+        '\n?mlagents/envs/communicator_objects/brain_parameters_proto.proto\x12\x14\x63ommunicator_objects\x1a\x39mlagents/envs/communicator_objects/resolution_proto.proto\x1a\x39mlagents/envs/communicator_objects/space_type_proto.proto"\xd4\x02\n\x14\x42rainParametersProto\x12\x1f\n\x17vector_observation_size\x18\x01 \x01(\x05\x12\'\n\x1fnum_stacked_vector_observations\x18\x02 \x01(\x05\x12\x1a\n\x12vector_action_size\x18\x03 \x03(\x05\x12\x41\n\x12\x63\x61mera_resolutions\x18\x04 \x03(\x0b\x32%.communicator_objects.ResolutionProto\x12"\n\x1avector_action_descriptions\x18\x05 \x03(\t\x12\x46\n\x18vector_action_space_type\x18\x06 \x01(\x0e\x32$.communicator_objects.SpaceTypeProto\x12\x12\n\nbrain_name\x18\x07 \x01(\t\x12\x13\n\x0bis_training\x18\x08 \x01(\x08\x42\x1f\xaa\x02\x1cMLAgents.CommunicatorObjectsb\x06proto3'
+    ),
+    dependencies=[
+        mlagents_dot_envs_dot_communicator__objects_dot_resolution__proto__pb2.DESCRIPTOR,
+        mlagents_dot_envs_dot_communicator__objects_dot_space__type__proto__pb2.DESCRIPTOR,
+    ],
+)
-  name='BrainParametersProto',
-  full_name='communicator_objects.BrainParametersProto',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='vector_observation_size', full_name='communicator_objects.BrainParametersProto.vector_observation_size', index=0,
-      number=1, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='num_stacked_vector_observations', full_name='communicator_objects.BrainParametersProto.num_stacked_vector_observations', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='vector_action_size', full_name='communicator_objects.BrainParametersProto.vector_action_size', index=2,
-      number=3, type=5, cpp_type=1, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='camera_resolutions', full_name='communicator_objects.BrainParametersProto.camera_resolutions', index=3,
-      number=4, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='vector_action_descriptions', full_name='communicator_objects.BrainParametersProto.vector_action_descriptions', index=4,
-      number=5, type=9, cpp_type=9, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='vector_action_space_type', full_name='communicator_objects.BrainParametersProto.vector_action_space_type', index=5,
-      number=6, type=14, cpp_type=8, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='brain_name', full_name='communicator_objects.BrainParametersProto.brain_name', index=6,
-      number=7, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='is_training', full_name='communicator_objects.BrainParametersProto.is_training', index=7,
-      number=8, type=8, cpp_type=7, label=1,
-      has_default_value=False, default_value=False,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=208,
-  serialized_end=548,
+    name="BrainParametersProto",
+    full_name="communicator_objects.BrainParametersProto",
+    filename=None,
+    file=DESCRIPTOR,
+    containing_type=None,
+    fields=[
+        _descriptor.FieldDescriptor(
+            name="vector_observation_size",
+            full_name="communicator_objects.BrainParametersProto.vector_observation_size",
+            index=0,
+            number=1,
+            type=5,
+            cpp_type=1,
+            label=1,
+            has_default_value=False,
+            default_value=0,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(
+            name="num_stacked_vector_observations",
+            full_name="communicator_objects.BrainParametersProto.num_stacked_vector_observations",
+            index=1,
+            number=2,
+            type=5,
+            cpp_type=1,
+            label=1,
+            has_default_value=False,
+            default_value=0,
+            message_type=None,
+            enum_type=None,
+            containing_type=None,
+            is_extension=False,
+            extension_scope=None,
+            serialized_options=None,
+            file=DESCRIPTOR,
+        ),
+        _descriptor.FieldDescriptor(