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: a802d0d7

文件历史

GitHub a802d0d7 Make SubprocessEnvManager take asynchronous steps (#2265 ) SubprocessEnvManager takes steps synchronously to reproduce old behavior, meaning all parallel environments will need to wait for the slowest environment to take a step. If some steps take much longer than others, this can lead to a substantial overall slowdown in practice. We've seen extreme cases where we see almost a 2x speedup from using asynchronous stepping, with no downside for our faster environments. (Bouncer 16% improvement, Walker 14% improvement in tests). This PR changes the SubprocessEnvManager to use async stepping. This means on the "step" call the environment manager will enqueue step requests to workers, and then only wait until at least one step has been completed before returning.		5 年前
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mlagents/envs	Make SubprocessEnvManager take asynchronous steps (#2265)	5 年前
README.md	Split `mlagents` into two packages (#1812)	6 年前
setup.py	Relax the cloudpickle version restriction (#2279)	5 年前

README.md

Unity ML-Agents Python Interface

The mlagents_envs Python package is part of the ML-Agents Toolkit. mlagents_envs provides a Python API that allows direct interaction with the Unity game engine. It is used by the trainer implementation in mlagents as well as the gym-unity package to perform reinforcement learning within Unity. mlagents_envs can be used independently of mlagents for Python communication.

The mlagents_envs Python package contains one sub package:

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.

Installation

Install the mlagents_envs package with:

pip install mlagents_envs

Usage & More Information

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