Skip to main content

Package to receive goal-directed environments

Project description

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/

Installing Extras

This package offers additional environments via optional extras. To install a specific environment extra, include it in the pip install command:

  • Minigrid Environment:
    Installs the minigrid dependency.

    pip install gr_envs[minigrid]
    
  • Panda Environment:
    Installs the panda_gym dependency.

    pip install gr_envs[panda]
    
  • Parking Environment:
    (Corresponds to the highway-env dependency.)

    pip install gr_envs[highway]
    
  • Point-Maze Environment:
    (Corresponds to the gymnasium-robotics dependency.)

    pip install gr_envs[maze]
    

Supported envs:

  • Minigrid
  • Panda
  • Parking
  • Point-Maze

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gr_envs-0.2.1.tar.gz (19.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

gr_envs-0.2.1-py3-none-any.whl (25.7 kB view details)

Uploaded Python 3

File details

Details for the file gr_envs-0.2.1.tar.gz.

File metadata

  • Download URL: gr_envs-0.2.1.tar.gz
  • Upload date:
  • Size: 19.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.13

File hashes

Hashes for gr_envs-0.2.1.tar.gz
Algorithm Hash digest
SHA256 116cc39ad20a0e92ffcb4839ee167548a2fa69b9317b5694b73cb71d6282a5ff
MD5 06e3867a26acb3c7df508483eda56e64
BLAKE2b-256 8739ad0b9ae15d1098c0fc52b510c5b45793ecc38b4116b5884e069e970a3aba

See more details on using hashes here.

File details

Details for the file gr_envs-0.2.1-py3-none-any.whl.

File metadata

  • Download URL: gr_envs-0.2.1-py3-none-any.whl
  • Upload date:
  • Size: 25.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.13

File hashes

Hashes for gr_envs-0.2.1-py3-none-any.whl
Algorithm Hash digest
SHA256 c97a4adaddbd916e1679667cc5bee97a1c24f44b0da4c383457f1c101c8678a5
MD5 f48daf7370618f3254ae3b0d1617925a
BLAKE2b-256 d195e2e7493bd355e52bde573ef6e9d051da983bfbb35753d81c26a8879bba81

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page