Metadata-Version: 2.1
Name: mlagents
Version: 0.20.0
Summary: Unity Machine Learning Agents
Home-page: https://github.com/Unity-Technologies/ml-agents
Author: Unity Technologies
Author-email: ML-Agents@unity3d.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6.1
Description-Content-Type: text/markdown
Requires-Dist: grpcio (>=1.11.0)
Requires-Dist: h5py (>=2.9.0)
Requires-Dist: mlagents-envs (==0.20.0)
Requires-Dist: numpy (<2.0,>=1.13.3)
Requires-Dist: Pillow (>=4.2.1)
Requires-Dist: protobuf (>=3.6)
Requires-Dist: pyyaml (>=3.1.0)
Requires-Dist: tensorflow (<3.0,>=1.14)
Requires-Dist: cattrs (>=1.0.0)
Requires-Dist: attrs (>=19.3.0)
Requires-Dist: six (>=1.12.0)
Requires-Dist: pypiwin32 (==223) ; platform_system == "Windows"
Provides-Extra: torch
Requires-Dist: torch (>=1.5.0) ; extra == 'torch'

# Unity ML-Agents Trainers

The `mlagents` Python package is part of the
[ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents). `mlagents`
provides a set of reinforcement and imitation learning algorithms designed to be
used with Unity environments. The algorithms interface with the Python API
provided by the `mlagents_envs` package. See [here](../docs/Python-API.md) for
more information on `mlagents_envs`.

The algorithms can be accessed using the: `mlagents-learn` access point. See
[here](../docs/Training-ML-Agents.md) for more information on using this
package.

## Installation

Install the `mlagents` package with:

```sh
pip3 install mlagents
```

## Usage & More Information

For more information on the ML-Agents Toolkit and how to instrument a Unity
scene with the ML-Agents SDK, check out the main
[ML-Agents Toolkit documentation](../docs/Readme.md).

## Limitations

- `mlagents` does not yet explicitly support multi-agent scenarios so training
  cooperative behavior among different agents is not stable.
- Resuming self-play from a checkpoint resets the reported ELO to the default
  value.


