# An Extension of MiniWorld
blocksworld3d is an enhancement on top of the MiniWorld environment, specifically focusing on the RoomObjects scenario. It's an open-source research project aimed at advancing the development of spatial reasoning and manipulation strategies in robotic systems.
# Blocksworld3D
blocksworld3d proposes a unique 3D environment that simulates the classic blocksworld problem except with an additional row of blocks. With various problem instances introducing different constraints on block positioning, start points, goals, and obstacles, this dynamic aspect fosters the exploration of spatial strategies in diverse settings.
The environment is minimalistic and designed for reinforcement learning & robotics research. It's compatible with the MiniWorld API, providing a seamless integration with existing toolsets.
## Installation
Install from ``PyPi`` using:
``pip install blocksworld3d``
Or install from the source using:
```
git clone https://github.com/ethanmclark1/blocksworld3d.git
cd blocksworld3d
pip install -r requirements.txt
pip install -e .
```
## Usage
```
import blocksworld3d
env = blocksworld3d.env()
env.reset(options={'problem_instance': 'stacking'}))
observation, _, terminations, truncations, _ = env.last()
env.step(action)
env.close()
```
## List of Problem Instances
| Problem Instance | Visualization |
| ---------------- | -------------------------------------------------------------- |
| `stacking` |  |
| `sorting` |  |
| `arranging` |  |
| ... | ... |
> **Note** : Customize the table above with the specific problem instances and visualization images relevant to your project.
## Contributing
We welcome contributions to blocksworld3d! Whether it's bug reports, feature requests, or pull requests, your collaboration helps make blocksworld3d better.
## Support
If you have questions or need support, please contact us at [eclark715@gmail.com](mailto:eclark715@gmail.com), or create an issue in the GitHub repository.
## License
blocksworld3d is open-source software licensed under the [MIT license](https://chat.openai.com/LINK_TO_YOUR_LICENSE).
## Paper Citation
If you found this environment helpful, consider citing relevant papers. Here's an example citation for the original MiniWorld environment:
<pre>
@article{MinigridMiniworld23,
author={Maxime Chevalier-Boisvert and Bolun Dai and Mark Towers and Rodrigo de Lazcano and Lucas Willems and Salem Lahlou and Suman Pal and Pablo Samuel Castro and Jordan Terry},
title={Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks},
journal={CoRR},
volume={abs/2306.13831}
year={2023}
}
</pre>
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"description": "# An Extension of MiniWorld\r\n\r\nblocksworld3d is an enhancement on top of the MiniWorld environment, specifically focusing on the RoomObjects scenario. It's an open-source research project aimed at advancing the development of spatial reasoning and manipulation strategies in robotic systems.\r\n\r\n# Blocksworld3D\r\n\r\nblocksworld3d proposes a unique 3D environment that simulates the classic blocksworld problem except with an additional row of blocks. With various problem instances introducing different constraints on block positioning, start points, goals, and obstacles, this dynamic aspect fosters the exploration of spatial strategies in diverse settings.\r\n\r\nThe environment is minimalistic and designed for reinforcement learning & robotics research. It's compatible with the MiniWorld API, providing a seamless integration with existing toolsets.\r\n\r\n## Installation\r\n\r\nInstall from ``PyPi`` using:\r\n\r\n``pip install blocksworld3d``\r\n\r\nOr install from the source using:\r\n\r\n```\r\ngit clone https://github.com/ethanmclark1/blocksworld3d.git\r\ncd blocksworld3d\r\npip install -r requirements.txt\r\npip install -e .\r\n```\r\n\r\n## Usage\r\n\r\n```\r\nimport blocksworld3d\r\n\r\nenv = blocksworld3d.env()\r\nenv.reset(options={'problem_instance': 'stacking'}))\r\nobservation, _, terminations, truncations, _ = env.last()\r\nenv.step(action)\r\nenv.close()\r\n```\r\n\r\n## List of Problem Instances\r\n\r\n| Problem Instance | Visualization |\r\n| ---------------- | -------------------------------------------------------------- |\r\n| `stacking` |  |\r\n| `sorting` |  |\r\n| `arranging` |  |\r\n| ... | ... |\r\n\r\n> **Note** : Customize the table above with the specific problem instances and visualization images relevant to your project.\r\n\r\n## Contributing\r\n\r\nWe welcome contributions to blocksworld3d! Whether it's bug reports, feature requests, or pull requests, your collaboration helps make blocksworld3d better.\r\n\r\n## Support\r\n\r\nIf you have questions or need support, please contact us at [eclark715@gmail.com](mailto:eclark715@gmail.com), or create an issue in the GitHub repository.\r\n\r\n## License\r\n\r\nblocksworld3d is open-source software licensed under the [MIT license](https://chat.openai.com/LINK_TO_YOUR_LICENSE).\r\n\r\n## Paper Citation\r\n\r\nIf you found this environment helpful, consider citing relevant papers. Here's an example citation for the original MiniWorld environment:\r\n\r\n<pre>\r\n@article{MinigridMiniworld23,\r\n author={Maxime Chevalier-Boisvert and Bolun Dai and Mark Towers and Rodrigo de Lazcano and Lucas Willems and Salem Lahlou and Suman Pal and Pablo Samuel Castro and Jordan Terry},\r\n title={Minigrid & Miniworld: Modular & Customizable Reinforcement Learning Environments for Goal-Oriented Tasks},\r\n journal={CoRR},\r\n volume={abs/2306.13831}\r\n year={2023}\r\n}\r\n</pre>\r\n",
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