GitHub
69d1a033
Develop remove past action communication (#2913)
* Modifying the .proto files * attempt 1 at refactoring Python * works for ppo hallway * changing the documentation * now works with both sac and ppo both training and inference * Ned to fix the tests * TODOs : - Fix the demonstration recorder - Fix the demonstration loader - verify the intrinsic reward signals work - Fix the tests on Python - Fix the C# tests * Regenerating the protos * fix proto typo * protos and modifying the C# demo recorder * modified the demo loader * Demos are loading * IMPORTANT : THESE ARE THE FILES USED FOR CONVERSION FROM OLD TO NEW FORMAT * Modified all the demo files * Fixing all the tests * fixing ci * addressing comments * removing reference to memories in the ll-api |
5 年前 | |
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mlagents | Develop remove past action communication (#2913) | 5 年前 |
README.md | Fixing tables in documentation and other markdown errors. (#1199) | 6 年前 |
setup.py | undo hacks | 5 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.