Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

3.2 KiB

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.

Linux Build Support

Windows Users

For setting up your environment on Windows, we have created a detailed guide to setting up your env. For Mac and Linux, continue with this guide.

Mac and Unix Users

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.

The ml-agents subdirectory contains Python packages which provide trainers and a Python API to interface with Unity.

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 along with the dependencies listed in the setup.py file. Some of the primary dependencies include:

Download and install Python 3.6 if you do not already have it.

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

To install the dependencies and mlagents Python package, enter the ml-agents/ subdirectory and run from the command line:

pip3 install -e .

If you installed this correctly, you should be able to run mlagents-learn --help

Notes:

  • We do not currently support Python 3.7 or Python 3.5.
  • If you are using Anaconda and are having trouble with TensorFlow, please see the following note on how to install TensorFlow in an Anaconda environment.

Docker-based Installation

If you'd like to use Docker for ML-Agents, please follow this guide.

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).