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 c258b1c3 Move 'take_action' into Policy class (#1669 ) * Move 'take_action' into Policy class This refactor is part of Actor-Trainer separation. Since policies will be distributed across actors in separate processes which share a single trainer, taking an action should be the responsibility of the policy. This change makes a few smaller changes: * Combines `take_action` logic between trainers, making it more generic * Adds an `ActionInfo` data class to be more explicit about the data returned by the policy, only used by TrainerController and policy for now. * Moves trainer stats logic out of `take_action` and into `add_experiences` * Renames 'take_action' to 'get_action'		6 年前
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mlagents	Move 'take_action' into Policy class (#1669)	6 年前
tests	Move 'take_action' into Policy class (#1669)	6 年前
README.md	Fixing tables in documentation and other markdown errors. (#1199)	6 年前
setup.py	added the pypiwin32 package (#1668)	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.