Split `mlagents` into two packages (#1812)

* Reogranize project

* Fix all tests

* Address comments

* Delete init file

* Update requirements

* Tick version

* Add timeout wait parameter (mlagents_envs) (#1699)

* Add timeout wait param

* Remove unnecessary function

* Add new meta files for communicator objects

* Fix all tests

* update circleci

* Reorganize mlagents_envs tests

* WIP: test removing circleci cache

* Move gym tests

* Namespaced packages

* Update installation instructions for separate packages

* Remove unused package from setup script

* Add Readme for ml-agents-envs

* Clarify docs and re-comment compiler in make.bat

* Add more doc to installation

* Add back fix for Hololens

* Recompile Protobufs

* Change mlagents_envs to mlagents.envs in trainer_controller

* Remove extraneous files, fix win bat script

* Support Python 3.7 for envs package

.circleci/config.yml

           command: |
             python3 -m venv venv
             . venv/bin/activate
-            cd ml-agents && pip install -e .
+            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
             cd ../gym-unity && pip install -e .
 

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/AgentActionProto.cs

       }
       if (other.customAction_ != null) {
         if (customAction_ == null) {
-          customAction_ = new global::MLAgents.CommunicatorObjects.CustomAction();
+          CustomAction = new global::MLAgents.CommunicatorObjects.CustomAction();
         }
         CustomAction.MergeFrom(other.CustomAction);
       }
           }
           case 42: {
             if (customAction_ == null) {
-              customAction_ = new global::MLAgents.CommunicatorObjects.CustomAction();
+              CustomAction = new global::MLAgents.CommunicatorObjects.CustomAction();
-            input.ReadMessage(customAction_);
+            input.ReadMessage(CustomAction);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/AgentInfoProto.cs

       actionMask_.Add(other.actionMask_);
       if (other.customObservation_ != null) {
         if (customObservation_ == null) {
-          customObservation_ = new global::MLAgents.CommunicatorObjects.CustomObservation();
+          CustomObservation = new global::MLAgents.CommunicatorObjects.CustomObservation();
         }
         CustomObservation.MergeFrom(other.CustomObservation);
       }
           }
           case 98: {
             if (customObservation_ == null) {
-              customObservation_ = new global::MLAgents.CommunicatorObjects.CustomObservation();
+              CustomObservation = new global::MLAgents.CommunicatorObjects.CustomObservation();
-            input.ReadMessage(customObservation_);
+            input.ReadMessage(CustomObservation);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/BrainParametersProto.cs

             break;
           }
           case 48: {
-            vectorActionSpaceType_ = (global::MLAgents.CommunicatorObjects.SpaceTypeProto) input.ReadEnum();
+            VectorActionSpaceType = (global::MLAgents.CommunicatorObjects.SpaceTypeProto) input.ReadEnum();
             break;
           }
           case 58: {

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/EnvironmentParametersProto.cs

       floatParameters_.Add(other.floatParameters_);
       if (other.customResetParameters_ != null) {
         if (customResetParameters_ == null) {
-          customResetParameters_ = new global::MLAgents.CommunicatorObjects.CustomResetParameters();
+          CustomResetParameters = new global::MLAgents.CommunicatorObjects.CustomResetParameters();
         }
         CustomResetParameters.MergeFrom(other.CustomResetParameters);
       }
           }
           case 18: {
             if (customResetParameters_ == null) {
-              customResetParameters_ = new global::MLAgents.CommunicatorObjects.CustomResetParameters();
+              CustomResetParameters = new global::MLAgents.CommunicatorObjects.CustomResetParameters();
-            input.ReadMessage(customResetParameters_);
+            input.ReadMessage(CustomResetParameters);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityInput.cs

       }
       if (other.rlInput_ != null) {
         if (rlInput_ == null) {
-          rlInput_ = new global::MLAgents.CommunicatorObjects.UnityRLInput();
+          RlInput = new global::MLAgents.CommunicatorObjects.UnityRLInput();
-          rlInitializationInput_ = new global::MLAgents.CommunicatorObjects.UnityRLInitializationInput();
+          RlInitializationInput = new global::MLAgents.CommunicatorObjects.UnityRLInitializationInput();
         }
         RlInitializationInput.MergeFrom(other.RlInitializationInput);
       }
             break;
           case 10: {
             if (rlInput_ == null) {
-              rlInput_ = new global::MLAgents.CommunicatorObjects.UnityRLInput();
+              RlInput = new global::MLAgents.CommunicatorObjects.UnityRLInput();
-            input.ReadMessage(rlInput_);
+            input.ReadMessage(RlInput);
-              rlInitializationInput_ = new global::MLAgents.CommunicatorObjects.UnityRLInitializationInput();
+              RlInitializationInput = new global::MLAgents.CommunicatorObjects.UnityRLInitializationInput();
-            input.ReadMessage(rlInitializationInput_);
+            input.ReadMessage(RlInitializationInput);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityMessage.cs

       }
       if (other.header_ != null) {
         if (header_ == null) {
-          header_ = new global::MLAgents.CommunicatorObjects.Header();
+          Header = new global::MLAgents.CommunicatorObjects.Header();
-          unityOutput_ = new global::MLAgents.CommunicatorObjects.UnityOutput();
+          UnityOutput = new global::MLAgents.CommunicatorObjects.UnityOutput();
-          unityInput_ = new global::MLAgents.CommunicatorObjects.UnityInput();
+          UnityInput = new global::MLAgents.CommunicatorObjects.UnityInput();
         }
         UnityInput.MergeFrom(other.UnityInput);
       }
             break;
           case 10: {
             if (header_ == null) {
-              header_ = new global::MLAgents.CommunicatorObjects.Header();
+              Header = new global::MLAgents.CommunicatorObjects.Header();
-            input.ReadMessage(header_);
+            input.ReadMessage(Header);
-              unityOutput_ = new global::MLAgents.CommunicatorObjects.UnityOutput();
+              UnityOutput = new global::MLAgents.CommunicatorObjects.UnityOutput();
-            input.ReadMessage(unityOutput_);
+            input.ReadMessage(UnityOutput);
-              unityInput_ = new global::MLAgents.CommunicatorObjects.UnityInput();
+              UnityInput = new global::MLAgents.CommunicatorObjects.UnityInput();
-            input.ReadMessage(unityInput_);
+            input.ReadMessage(UnityInput);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityOutput.cs

       }
       if (other.rlOutput_ != null) {
         if (rlOutput_ == null) {
-          rlOutput_ = new global::MLAgents.CommunicatorObjects.UnityRLOutput();
+          RlOutput = new global::MLAgents.CommunicatorObjects.UnityRLOutput();
-          rlInitializationOutput_ = new global::MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
+          RlInitializationOutput = new global::MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
         }
         RlInitializationOutput.MergeFrom(other.RlInitializationOutput);
       }
             break;
           case 10: {
             if (rlOutput_ == null) {
-              rlOutput_ = new global::MLAgents.CommunicatorObjects.UnityRLOutput();
+              RlOutput = new global::MLAgents.CommunicatorObjects.UnityRLOutput();
-            input.ReadMessage(rlOutput_);
+            input.ReadMessage(RlOutput);
-              rlInitializationOutput_ = new global::MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
+              RlInitializationOutput = new global::MLAgents.CommunicatorObjects.UnityRLInitializationOutput();
-            input.ReadMessage(rlInitializationOutput_);
+            input.ReadMessage(RlInitializationOutput);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityRlInitializationOutput.cs

       brainParameters_.Add(other.brainParameters_);
       if (other.environmentParameters_ != null) {
         if (environmentParameters_ == null) {
-          environmentParameters_ = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
+          EnvironmentParameters = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
         }
         EnvironmentParameters.MergeFrom(other.EnvironmentParameters);
       }
           }
           case 50: {
             if (environmentParameters_ == null) {
-              environmentParameters_ = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
+              EnvironmentParameters = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
-            input.ReadMessage(environmentParameters_);
+            input.ReadMessage(EnvironmentParameters);
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityRlInput.cs

       agentActions_.Add(other.agentActions_);
       if (other.environmentParameters_ != null) {
         if (environmentParameters_ == null) {
-          environmentParameters_ = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
+          EnvironmentParameters = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
         }
         EnvironmentParameters.MergeFrom(other.EnvironmentParameters);
       }
           }
           case 18: {
             if (environmentParameters_ == null) {
-              environmentParameters_ = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
+              EnvironmentParameters = new global::MLAgents.CommunicatorObjects.EnvironmentParametersProto();
-            input.ReadMessage(environmentParameters_);
+            input.ReadMessage(EnvironmentParameters);
             break;
           }
           case 24: {
           case 32: {
-            command_ = (global::MLAgents.CommunicatorObjects.CommandProto) input.ReadEnum();
+            Command = (global::MLAgents.CommunicatorObjects.CommandProto) input.ReadEnum();
             break;
           }
         }

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/UnityToExternalGrpc.cs

 
 # if UNITY_EDITOR || UNITY_STANDALONE_WIN || UNITY_STANDALONE_OSX || UNITY_STANDALONE_LINUX
 
-#pragma warning disable 1591
+#pragma warning disable 0414, 1591
+
+
 
 namespace MLAgents.CommunicatorObjects {
   public static partial class UnityToExternal
 }
 #endregion
 
-#endif 
-
+#endif

docs/Installation-Windows.md

 It also contains many [example environments](Learning-Environment-Examples.md)
 to help you get started.
 
-The `ml-agents` subdirectory contains Python packages which provide
-trainers and a Python API to interface with Unity.
+The `ml-agents` subdirectory contains a Python package which provides deep reinforcement 
+learning trainers to use with Unity environments.
-The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.
-
-In our example, the files are located in `C:\Downloads`. After you have either
-cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents
-subdirectory inside the ml-agents directory:
+The `ml-agents-envs` subdirectory contains a Python API to interface with Unity, which
+the `ml-agents` package depends on. 
-```console
-cd C:\Downloads\ml-agents\ml-agents
-```
+The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.
+Keep in mind where the files were downloaded, as you will need the 
+trainer config files in this directory when running `mlagents-learn`.
-Prompt within `ml-agents` subdirectory:
+Prompt:
-```sh
-pip install -e .
+```console
+pip install mlagents
 ```
 
 This will complete the installation of all the required Python packages to run
 
-```sh
-pip install -e . --no-cache-dir
+```console
+pip install mlagents --no-cache-dir
+
+### Installing for Development
+
+If you intend to make modifications to `ml-agents` or `ml-agents-envs`, you should install 
+the packages from the cloned repo rather than from PyPi. To do this, you will need to install
+ `ml-agents` and `ml-agents-envs` separately. 
+ 
+In our example, the files are located in `C:\Downloads`. After you have either
+cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents
+subdirectory inside the ml-agents directory:
+
+```console
+cd C:\Downloads\ml-agents
+```
+ 
+From the repo's main directory, now run:
+
+```console
+cd ml-agents-envs
+pip install -e .
+cd ..
+cd ml-agents
+pip install -e .
+```
+
+Running pip with the `-e` flag will let you make changes to the Python files directly and have those
+reflected when you run `mlagents-learn`. It is important to install these packages in this order as the
+`mlagents` package depends on `mlagents_envs`, and installing it in the other 
+order will download `mlagents_envs` from PyPi. 
 
 ## (Optional) Step 4: GPU Training using The ML-Agents Toolkit
 

docs/Installation.md

 It also contains many [example environments](Learning-Environment-Examples.md)
 to help you get started.
 
-The `ml-agents` subdirectory contains Python packages which provide
-trainers and a Python API to interface with Unity.
+The `ml-agents` subdirectory contains a Python package which provides deep reinforcement 
+learning trainers to use with Unity environments.
+
+The `ml-agents-envs` subdirectory contains a Python API to interface with Unity, which
+the `ml-agents` package depends on. 
 
 The `gym-unity` subdirectory contains a package to interface with OpenAI Gym.
 
 [instructions](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
 on installing it.
 
-To install the dependencies and `mlagents` Python package, enter the
-`ml-agents/` subdirectory and run from the command line:
+To install the dependencies and `mlagents` Python package, run from the command line:
-pip3 install -e .
+pip3 install mlagents
+Note that this will install `ml-agents` from PyPi, _not_ from the cloned repo. 
-`mlagents-learn --help`
+`mlagents-learn --help`, after which you will see the Unity logo and the command line
+parameters you can use with `mlagents-learn`. 
 
 **Notes:**
 
   [link](https://www.tensorflow.org/install/pip)
   on how to install TensorFlow in an Anaconda environment.
+
+### Installing for Development
+
+If you intend to make modifications to `ml-agents` or `ml-agents-envs`, you should install 
+the packages from the cloned repo rather than from PyPi. To do this, you will need to install
+ `ml-agents` and `ml-agents-envs` separately. Do this by running (starting from the repo's main
+ directory):
+
+```sh
+cd ml-agents-envs
+pip3 install -e ./
+cd ..
+cd ml-agents
+pip3 install -e ./
+```
+
+Running pip with the `-e` flag will let you make changes to the Python files directly and have those
+reflected when you run `mlagents-learn`. It is important to install these packages in this order as the
+`mlagents` package depends on `mlagents_envs`, and installing it in the other 
+order will download `mlagents_envs` from PyPi. 
+
 
 ## Docker-based Installation
 

gym-unity/setup.py

       author_email='ML-Agents@unity3d.com',
       url='https://github.com/Unity-Technologies/ml-agents',
       packages=find_packages(),
-      install_requires=['gym', 'mlagents']
+      install_requires=['gym', 'mlagents_envs']
       )

ml-agents/mlagents/trainers/models.py

 import tensorflow as tf
 import tensorflow.contrib.layers as c_layers
 
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class LearningModel(object):

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

 import tensorflow as tf
 from mlagents.trainers.models import LearningModel
 
-logger = logging.getLogger("mlagents.envs")
+logger = logging.getLogger("mlagents.trainers")
 
 
 class PPOModel(LearningModel):

ml-agents/mlagents/trainers/trainer.py

 
 
 class Trainer(object):
-    """This class is the base class for the mlagents.trainers"""
+    """This class is the base class for the mlagents.envs.trainers"""
 
     def __init__(self, brain, trainer_parameters, training, run_id):
         """

ml-agents/setup.py

         'Programming Language :: Python :: 3.6'
     ],
 
-    packages=find_packages(exclude=['tests', 'tests.*', '*.tests', '*.tests.*']),  # Required
+    packages=['mlagents.trainers'],  # Required
+    zip_safe=False,
+        'mlagents_envs==0.7.0',
         'tensorflow>=1.7,<1.8',
         'Pillow>=4.2.1',
         'matplotlib',

protobuf-definitions/make.bat

 
 SRC_DIR=proto/mlagents/envs/communicator_objects
 DST_DIR_C=../UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects
-DST_DIR_P=../ml-agents
+DST_DIR_P=../ml-agents-envs
 PROTO_PATH=proto
 
 PYTHON_PACKAGE=mlagents/envs/communicator_objects

protobuf-definitions/make_for_win.bat

 
 set SRC_DIR=proto\mlagents\envs\communicator_objects
 set DST_DIR_C=..\UnitySDK\Assets\ML-Agents\Scripts\CommunicatorObjects
-set DST_DIR_P=..\ml-agents
+set DST_DIR_P=..\ml-agents-envs
 set PROTO_PATH=proto
 
 set PYTHON_PACKAGE=mlagents\envs\communicator_objects

gym-unity/gym_unity/tests/test_gym.py

 from gym import spaces
 from gym_unity.envs import UnityEnv, UnityGymException
 
-# Tests
-
 
 @mock.patch('gym_unity.envs.unity_env.UnityEnvironment')
 def test_gym_wrapper(mock_env):
     # Avoid using mutable object as default param
     if vector_action_space_size is None:
         vector_action_space_size = [2]
-    mock_brain = mock.Mock();
+    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.vector_action_space_type = vector_action_space_type

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

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/agent_action_proto.proto
 

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

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/agent_info_proto.proto
 

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

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/brain_parameters_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/command_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/command_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/custom_action_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/custom_action.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/custom_observation_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/custom_observation.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/custom_reset_parameters_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/custom_reset_parameters.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/demonstration_meta_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/demonstration_meta_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/engine_configuration_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/engine_configuration_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/environment_parameters_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/environment_parameters_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/header_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/header.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/resolution_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/resolution_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/space_type_proto_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/space_type_proto.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_input_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_input.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_message_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_message.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_output_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_output.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_input_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_rl_initialization_input.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_initialization_output_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_rl_initialization_output.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_input_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_rl_input.proto
 

ml-agents-envs/mlagents/envs/communicator_objects/unity_rl_output_pb2.py

+# -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: mlagents/envs/communicator_objects/unity_rl_output.proto
 

ml-agents-envs/mlagents/envs/tests/test_envs.py

 import unittest.mock as mock
 import pytest
-import struct
-from tests.mock_communicator import MockCommunicator
+from mlagents.envs.mock_communicator import MockCommunicator
 
 @mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
 def test_handles_bad_filename(get_communicator):

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

 from mlagents.trainers.bc.models import BehavioralCloningModel
 from mlagents.trainers.bc.policy import BCPolicy
 from mlagents.envs import UnityEnvironment
-from tests.mock_communicator import MockCommunicator
+from mlagents.envs.mock_communicator import MockCommunicator
 
 
 @pytest.fixture

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

 from mlagents.trainers.ppo.trainer import discount_rewards
 from mlagents.trainers.ppo.policy import PPOPolicy
 from mlagents.envs import UnityEnvironment
-from tests.mock_communicator import MockCommunicator
+from mlagents.envs.mock_communicator import MockCommunicator
 
 
 @pytest.fixture

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

-from mlagents.envs.communicator import Communicator
-from mlagents.envs.communicator_objects import UnityMessage, UnityOutput, UnityInput, \
+from .communicator import Communicator
+from .communicator_objects import UnityOutput, UnityInput, \
     ResolutionProto, BrainParametersProto, UnityRLInitializationOutput, \
     AgentInfoProto, UnityRLOutput
 

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/CustomAction.cs.meta

+fileFormatVersion: 2
+guid: a8d11b50ed9ce45f7827f5117b65db06
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/CustomObservation.cs.meta

+fileFormatVersion: 2
+guid: 896847c1364a7475d9094058ff93b7f0
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

UnitySDK/Assets/ML-Agents/Scripts/CommunicatorObjects/CustomResetParameters.cs.meta

+fileFormatVersion: 2
+guid: a071e48ae56b2424e8b59aad01646f59
+MonoImporter:
+  externalObjects: {}
+  serializedVersion: 2
+  defaultReferences: []
+  executionOrder: 0
+  icon: {instanceID: 0}
+  userData: 
+  assetBundleName: 
+  assetBundleVariant: 

ml-agents-envs/README.md

+# Unity ML-Agents Python Interface
+
+The `mlagents_envs` Python package is part of the
+[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents).
+`mlagents_envs` provides a Python API that allows direct interaction with the Unity
+game engine. It is used by the trainer implementation in `mlagents` as well as 
+the `gym-unity` package to perform reinforcement learning within Unity. `mlagents_envs` can be 
+used independently of `mlagents` for Python communication. 
+
+The `mlagents_envs` Python package contains one sub package:
+
+* `mlagents.envs`: A low level API which allows you to interact directly with a
+  Unity Environment. See
+  [here](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Python-API.md)
+  for more information on using this package.
+
+## Installation
+
+Install the `mlagents_envs` package with:
+
+```sh
+pip install mlagents_envs
+```
+
+## Usage & More Information
+
+For more detailed documentation, check out the
+[ML-Agents Toolkit documentation.](https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Readme.md)

ml-agents-envs/setup.py

+from setuptools import setup
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+setup(
+    name='mlagents_envs',
+    version='0.7.0',
+    description='Unity Machine Learning Agents Interface',
+    url='https://github.com/Unity-Technologies/ml-agents',
+    author='Unity Technologies',
+    author_email='ML-Agents@unity3d.com',
+
+    classifiers=[
+        'Intended Audience :: Developers',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: Python :: 3.6'
+    ],
+
+    packages=['mlagents.envs'],  # Required
+    zip_safe=False,
+
+    install_requires=[
+        'Pillow>=4.2.1,<=5.4.1',
+        'numpy>=1.13.3,<=1.16.1',
+        'pytest>=3.2.2,<4.0.0',
+        'protobuf>=3.6,<3.7',
+        'grpcio>=1.11.0,<1.12.0'],
+
+    python_requires=">=3.5,<3.8",
+)