Metadata-Version: 2.4
Name: gr_envs
Version: 0.1.2
Summary: Package to receive goal-directed environments
Author: Osher Elhadad, Ben Nageris
Author-email: Matan Shamir <matan.shamir@live.biu.ac.il>
License-Expression: MIT
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
Requires-Dist: minigrid
Requires-Dist: highway-env
Requires-Dist: tensorboardX
Requires-Dist: torchvision
Requires-Dist: panda_gym
Requires-Dist: rl_zoo3
Requires-Dist: gymnasium
Requires-Dist: gymnasium-robotics
Requires-Dist: stable_baselines3[extra]
Requires-Dist: sb3_contrib

# GREnvs
Gym Environments adjusted to Goal Recognition tasks.

## Installation
This repo is installable.
The name of the package is gr_envs.
The package serves as an extension with multiple gym environments and registration bundles that specifically fit GR frameworks, namely they are goal-conditioned.

The repo is distributed to Pypi.
to install the repo:
`pip install gr_envs`

Installing the repo registers the environments to gym, effectively enabling you to run your script\framework having the environments existing out-of-the-box.

If you're on windows and using vscode, you will need Microsoft Visual C++ 14.0 or greater. you can download a latest version here: https://visualstudio.microsoft.com/visual-cpp-build-tools/

## Contributing
Contributions are welcome! Please feel free to submit a pull request or open an issue if you have any suggestions or improvements.

## License
This project is licensed under the MIT License.
