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

目录树: 3b702792

文件历史

GitHub b6c97cb6 Fix for divide-by-zero error with Discrete Actions (#1520 ) * Enable buffer padding to be set other than 0 Allows buffer padding in AgentBufferField to be set to a custom value. In particular, 0-padding for `action_masks` causes a divide-by-zero error, and should be padded with 1’s instead. This is done as a parameter passed to the `append` method, so that the pad value can be set right after the instantiation of an AgentBufferField.		6 年前
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mlagents	Fix for divide-by-zero error with Discrete Actions (#1520)	6 年前
tests	Merge pull request #1494 from Unity-Technologies/release-v0.6	6 年前
README.md	Fixing tables in documentation and other markdown errors. (#1199)	6 年前
setup.py	Merge branch 'develop' into release-v0.6	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.