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。

unity3d unity unity-tech reinforcement-le deep-learning deep-reinforcement-learning neural-networks

文件历史

GitHub b05c9ac1 Add environment manager for parallel environments (#2209 ) Previously in v0.8 we added parallel environments via the SubprocessUnityEnvironment, which exposed the same abstraction as UnityEnvironment while actually wrapping many parallel environments via subprocesses. Wrapping many environments with the same interface as a single environment had some downsides, however: * Ordering needed to be preserved for agents across different envs, complicating the SubprocessEnvironment logic * Asynchronous environments with steps taken out of sync with the trainer aren't viable with the Environment abstraction This PR introduces a new EnvManager abstraction which exposes a reduced subset of the UnityEnvironment abstraction and a SubprocessEnvManager implementation which replaces the SubprocessUnityEnvironment.		6 年前
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mlagents/trainers	Add environment manager for parallel environments (#2209)	6 年前
README.md	Fixing tables in documentation and other markdown errors. (#1199)	7 年前
setup.py	Fixed the import issue (#2158)	6 年前

README.md

Unity ML-Agents Python Interface and Trainers

The mlagents Python package is part of the ML-Agents Toolkit. mlagents provides a Python API that allows direct interaction with the Unity game engine as well as a collection of trainers and algorithms to train agents in Unity environments.

The mlagents Python package contains two sub packages:

mlagents.envs: A low level API which allows you to interact directly with a Unity Environment. See here for more information on using this package.
mlagents.trainers: A set of Reinforcement Learning algorithms designed to be used with Unity environments. Access them using the: mlagents-learn access point. See here for more information on using this package.

Installation

Install the mlagents package with:

pip install mlagents

Usage & More Information

For more detailed documentation, check out the ML-Agents Toolkit documentation.