GitHub
7b69bd14
Refactor Trainer and Model (#2360)
- Move common functions to trainer.py, model.pyfromppo/trainer.py, ppo/policy.pyandppo/model.py' - Introduce RLTrainer class and move most of add_experiences and some common reward signal code there. PPO and SAC will inherit from this, not so much BC Trainer. - Add methods to Buffer to enable sampling, truncating, and save/loading. - Add scoping to create encoders in model.py |
6 年前 | |
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mlagents/trainers | Refactor Trainer and Model (#2360) | 6 年前 |
README.md | Fixing tables in documentation and other markdown errors. (#1199) | 7 年前 |
setup.py | Refactor Trainer and Model (#2360) | 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-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.