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v0.1
ml-agents/ml-agents
GitHub
517e3a0a
Remove env creation logic from TrainerController (#1562)
* Remove env creation logic from TrainerController Currently TrainerController includes logic related to creating the UnityEnvironment, which causes poor separation of concerns between the learn.py application script, TrainerController and UnityEnvironment: * TrainerController must know about the proper way to instantiate the UnityEnvironment, which may differ from application to application. This also makes mocking or subclassing UnityEnvironment more difficult. * Many arguments are passed by learn.py to TrainerController and passed along to UnityEnvironment. This change moves environment construction logic into learn.py, as part of the greater refactor to separate trainer logic from actor / environment. |
6 年前 | |
|---|---|---|
| .. | ||
| mlagents | Remove env creation logic from TrainerController (#1562) | 6 年前 |
| tests | Remove env creation logic from TrainerController (#1562) | 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-learnaccess 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.