Addressing Arthur's PR comments.

- Renamed `ml-agents-protobuf` to `protobuf-definitions`.
- Renamed `python/mlagents` to `python/ml-agents`.
- Removed comments from setup.py.

Dockerfile

 RUN apt-get install -y xvfb
 
 
-COPY python/mlagents/requirements.txt .
+COPY python/ml-agents/requirements.txt .
-COPY python/mlagents /mlagents
-WORKDIR /mlagents
+COPY python/ml-agents /ml-agents
+WORKDIR /ml-agents
 RUN pip install .
 
 # port 5005 is the port used in in Editor training.

docs/Installation.md

     git clone https://github.com/Unity-Technologies/ml-agents.git
 
 The `MLAgentsSDK` directory in this repository contains the Unity Assets
-to add to your projects. The `mlagents` directory contains the python package
-which provides trainers and a python API to interface with Unity.
+to add to your projects. The `python` directory contains python packages
+which provide trainers, a python API to interface with Unity, and a package
+to interface with OpenAI Gym.
-the dependencies listed in the [requirements file](../python/mlagents/requirements.txt).
+the dependencies listed in the [requirements file](../python/ml-agents/requirements.txt).
 Some of the primary dependencies include:
 
 - [TensorFlow](Background-TensorFlow.md)
 [instructions](https://packaging.python.org/guides/installing-using-linux-tools/#installing-pip-setuptools-wheel-with-linux-package-managers)
 on installing it.
 
-To install dependencies, enter the `python/mlagents/` directory and run from
+To install dependencies, enter the `python/ml-agents/` directory and run from
 the command line:
 
     pip install -r requirements.txt

docs/Learning-Environment-Executable.md

 
 **Note**: If you're using Anaconda, don't forget to activate the ml-agents environment first.
 
-If the `mlagents-learn` runs correctly and starts training, you should see something like this:
+If `mlagents-learn` runs correctly and starts training, you should see something like this:
 
 ![Training running](images/training-running.png)
 

docs/Python-API.md

 - **BrainParameters** — describes the data elements in a BrainInfo object. For
   example, provides the array length of an observation in BrainInfo.
 
-These classes are all defined in the `python/mlagents/mlagents/envs` folder of
+These classes are all defined in the `python/ml-agents/mlagents/envs` folder of
 the ML-Agents SDK.
 
 To communicate with an agent in a Unity environment from a Python program, the
 ## Loading a Unity Environment
 
 Python-side communication happens through `UnityEnvironment` which is located in
-`python/mlagents/mlagents/envs`. To load a Unity environment from a built binary
+`python/ml-agents/mlagents/envs`. To load a Unity environment from a built binary
 file, put the file in the same directory as `envs`. For example, if the filename
 of your Unity environment is 3DBall.app, in python, run:
 

docs/Training-ML-Agents.md

 The output of the training process is a model file containing the optimized policy. This model file is a TensorFlow data graph containing the mathematical operations and the optimized weights selected during the training process. You can use the generated model file with the Internal Brain type in your Unity project to decide the best course of action for an agent. 
 
 Use the command `mlagents-learn` to train your agents. This command is installed with the `mlagents` package
-and its implementation can be found at `python/mlagents/learn.py`. The [configuration file](#training-config-file), `trainer_config.yaml` specifies the hyperparameters used during training. You can edit this file with a text editor to add a specific configuration for each brain.
+and its implementation can be found at `python/ml-agents/learn.py`. The [configuration file](#training-config-file), `trainer_config.yaml` specifies the hyperparameters used during training. You can edit this file with a text editor to add a specific configuration for each brain.
 
 For a broader overview of reinforcement learning, imitation learning and the ML-Agents training process, see [ML-Agents Toolkit Overview](ML-Agents-Overview.md).
 

protobuf-defintions/make.bat

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

python/ml-agents/setup.py

+from setuptools import setup, find_packages
+from os import path
+from io import open
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the README file
+with open(path.join(here, '../../README.md'), encoding='utf-8') as f:
+    long_description = f.read()
+
+setup(
+    name='mlagents',
+    version='0.4.0',
+    description='Unity Machine Learning Agents',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    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=find_packages(exclude=['tests', 'tests.*', '*.tests', '*.tests.*']),  # Required
+
+    install_requires=[
+        'tensorflow>=1.7',
+        'Pillow>=4.2.1',
+        'matplotlib',
+        'numpy>=1.13.3,<=1.14.5',
+        'jupyter',
+        'pytest>=3.2.2',
+        'docopt',
+        'pyyaml',
+        'protobuf>=3.6.0',
+        'grpcio>=1.11.0'],
+
+    entry_points={
+        'console_scripts': [
+            'mlagents-learn=mlagents.learn:main',
+        ],
+    },
+)