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: f82f0f37

文件历史

GitHub f82f0f37 Get timers from subprocess (#2268 ) * Timer proof-of-concept * micro optimizations * add some timers * cleanup, add asserts * Cleanup (no start/end methods) and handle exceptions * unit test and decorator * move output code, add a decorator * cleanup * module docstring * actually write the timings when done with training * use __qualname__ instead * add a few more timers * fix mock import * fix unit test * get timers from worker process (WIP) * clean up timer merging * typo * WIP * cleanup merging code * bad merge * undo accidental change * remove reset command * fix style * fix unit tests * fix unit tests (they got overwrote in merge) * get timer root though a function * timer around communicate		5 年前
..
mlagents/envs	Get timers from subprocess (#2268)	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.