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 2d68b835 Check that worker port is available in RpcCommunicator (#1415 ) * Check that worker port is available in RpcCommunicator Previously the RpcCommunicator did not check the port or create the RPC server until `initialize()` was called. Since "initialize" requires the environment to be available, this means we might create a new environment which connects to an existing RPC server running in another process. This causes both training runs to fail. As a remedy to this issue, this commit moves the server creation into the RpcCommunicator constructor and adds an explicit socket binding check to the requested port. * Fixes suggested by Codacy * Update rpc_communicator.py * Addressing feedback: formatting & consistency		6 年前
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mlagents	Check that worker port is available in RpcCommunicator (#1415)	6 年前
tests	Check that worker port is available in RpcCommunicator (#1415)	6 年前
README.md	Fixing tables in documentation and other markdown errors. (#1199)	6 年前
requirements.txt	New proto files	7 年前
setup.py	Fixing learn.py, trainer_controller.py, and Docker (#1164)	7 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.