ml-agents/docs/Installation.md

Installation

To install and use ML-Agents, you need to install Unity, clone this repository and install Python with additional dependencies. Each of the subsections below overviews each step, in addition to a Docker set-up.

Install Unity 2017.4 or Later

Download and install Unity. If you would like to use our Docker set-up (introduced later), make sure to select the Linux Build Support component when installing Unity.

Environment Setup

We now support a single mechanism for installing ML-Agents on Mac/Windows/Linux using Virtual Environments. For more information on Virtual Environments and installation instructions, follow this guide.

Clone the ML-Agents Toolkit Repository

Once installed, you will want to clone the ML-Agents Toolkit GitHub repository.

git clone https://github.com/Unity-Technologies/ml-agents.git

The UnitySDK subdirectory contains the Unity Assets to add to your projects. It also contains many example environments to help you get started.

Package Installation

If you intend to copy the UnitySDK folder in to your project, ensure that you have the Barracuda preview package installed.

To install the Barracuda package in Unity 2017.4.x, you will have to copy the UnityPackageManager folder under the UnitySDK folder to the root directory of your project.

To install the Barrcuda package in later versions of Unity, navigate to the Package Manager window by navigating to the menu Window -> Package Manager. Click on the Adavanced dropdown menu to the left of the search bar and make sure "Show Preview Packages" is checked. Search for or select the Barracuda package and install the latest version.

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.

Install Python and mlagents Package

In order to use ML-Agents toolkit, you need Python 3.6.1 or higher. Download and install the latest version of Python if you do not already have it.

If your Python environment doesn't include pip3, see these instructions on installing it.

To install the mlagents Python package, run from the command line:

pip3 install mlagents

Note that this will install ml-agents from PyPi, not from the cloned repo. If you installed this correctly, you should be able to run mlagents-learn --help, after which you will see the Unity logo and the command line parameters you can use with mlagents-learn.

By installing the mlagents package, the dependencies listed in the setup.py file are also installed. Some of the primary dependencies include:

• TensorFlow (Requires a CPU w/ AVX support)
• Jupyter

Notes:

• We do not currently support Python 3.5 or lower.
• If you are using Anaconda and are having trouble with TensorFlow, please see the following link 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. From the repo's root directory, run:

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.

Next Steps

The Basic Guide page contains several short tutorials on setting up the ML-Agents toolkit within Unity, running a pre-trained model, in addition to building and training environments.

Help

If you run into any problems regarding ML-Agents, refer to our FAQ and our Limitations pages. If you can't find anything please submit an issue and make sure to cite relevant information on OS, Python version, and exact error message (whenever possible).